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Chang CP, Huang YC, Tsai YH, Lin LC, Yang JT, Wu KH, Wu PH, Peng SJ. Radiomics-based MRI model to predict hypoperfusion in lacunar infarction. Magn Reson Imaging 2025; 120:110366. [PMID: 40122187 DOI: 10.1016/j.mri.2025.110366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 02/19/2025] [Accepted: 03/02/2025] [Indexed: 03/25/2025]
Abstract
BACKGROUND Approximately 20-30 % of patients with acute ischemic stroke due to lacunar infarction experience early neurological deterioration (END) within the first three days after onset, leading to disability or more severe sequelae. Hemodynamic perfusion deficits may play a crucial role in END, causing growth in the infarcted area and functional impairments, and even poor long-term prognosis. Therefore, it is vitally important to predict which patients may be at risk of perfusion deficits to initiate treatment and close monitoring early, preparing for potential reperfusion. Our goal is to utilize radiomic features from magnetic resonance imaging (MRI) and machine learning techniques to develop a predictive model for hypoperfusion. METHOD During January 2011 to December 2020, a retrospective collection of 92 patients with lacunar stroke was conducted, who underwent MRI within 48 h, had clinical laboratory values, follow-up prognosis records, and advanced perfusion image to confirm the presence of hypoperfusion. Using the initial MRI of these patients, radiomics features were extracted and selected from Diffusion Weighted Imaging (DWI), Apparent Diffusion Coefficient (ADC), and Fluid Attenuated Inversion Recovery (FLAIR) sequences. The data was divided into an 80 % training set and a 20 % testing set, and a hypoperfusion prediction model was developed using machine learning. RESULT Tthe model trained on DWI + FLAIR sequence showed superior performance with an accuracy of 84.1 %, AUC 0.92, recall 79.5 %, specificity 87.8 %, precision 83.8 %, and F1 score 81.2. Statistically significant clinical factors between patients with and without hypoperfusion included the NIHSS scores and the size of the lacunar infarction. Combining these two features with the top seven weighted radiomics features from DWI + FLAIR sequence, a total of nine features were used to develop a new prediction model through machine learning. This model in test set achieved an accuracy of 88.9 %, AUC 0.91, recall 87.5 %, specificity 90.0 %, precision 87.5 %, and F1 score 87.5. CONCLUSION Utilizing radiomics techniques on DWI and FLAIR sequences from MRI of patients with lacunar stroke, it is possible to predict the presence of hypoperfusion, necessitating close monitoring to prevent the deterioration of clinical symptoms. Incorporating stroke volume and NIHSS scores into the prediction model enhances its performance. Future studies of a larger scale are required to validate these findings.
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Affiliation(s)
- Chia-Peng Chang
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan; Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan; In-service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University
| | - Yen-Chu Huang
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Neurology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
| | - Yuan-Hsiung Tsai
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Leng-Chieh Lin
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan.
| | - Jen-Tsung Yang
- College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Neurosurgery, Chang Gung Memorial Hospital, Chiayi, Taiwan.
| | - Kai-Hsiang Wu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan; Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan
| | - Po-Han Wu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Syu-Jyun Peng
- In-service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
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Stocker D, Hectors S, Marinelli B, Carbonell G, Bane O, Hulkower M, Kennedy P, Ma W, Lewis S, Kim E, Wang P, Taouli B. Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach. Abdom Radiol (NY) 2025; 50:2000-2011. [PMID: 39460801 PMCID: PMC11991973 DOI: 10.1007/s00261-024-04606-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 09/04/2024] [Accepted: 09/17/2024] [Indexed: 10/28/2024]
Abstract
PURPOSE To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. METHODS This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. RESULTS A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). CONCLUSION The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.
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Affiliation(s)
- Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, University Hospital Virgen de la Arrixaca, Murcia, Spain
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Hulkower
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Weiping Ma
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edward Kim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pei Wang
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Zhang W, Zhuang D, Wei W, Yang Y, Ma L, Du H, Jin A, He J, Li X. The 100 most-cited radiomics articles in cancer research: A bibliometric analysis. Clin Imaging 2025; 121:110442. [PMID: 40086035 DOI: 10.1016/j.clinimag.2025.110442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 02/15/2025] [Accepted: 03/06/2025] [Indexed: 03/16/2025]
Abstract
Radiomics, an advanced medical imaging analysis technique introduced by Professor Lambin in 2012, has quickly become a key area of medical research. To explore emerging trends in cancer-related radiomics, we conducted a bibliometric analysis of the 100 most-cited articles (T100) in this field. Data were collected from the Web of Science Core Collection on October 7, 2023, and the articles were ranked by citation count. We extracted data such as authors, journals, citation counts, and publication years and analyzed it using Microsoft Excel 2019 and R 4.4.2. CiteSpace was used to create co-occurrence and citation burst maps to show the relationships between authors, countries, institutions, and keywords. The analysis revealed that the T100 came from 81 countries, with the U.S. contributing the most (56 articles). Harvard University was the leading institution, and the journal Radiology had the highest citation count. Aerts Hugo JWL was the most influential author. The study highlights that "lung cancer" and "artificial intelligence" are emerging as major research hotspots, shaping the future of cancer radiomics.
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Affiliation(s)
- Wenhao Zhang
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China; Department of Clinical Medical, First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Dongmei Zhuang
- Suzhou Hospital of Anhui Medical University, Suzhou, Anhui, China
| | - Wenzhuo Wei
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Yuchen Yang
- Department of Clinical Medical, First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Lijun Ma
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - He Du
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Anran Jin
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Jingyi He
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Xiaoming Li
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China.
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Yan F, Zhang Q, Mutembei BM, Wang C, Alhajeri ZA, Pandit K, Zhang F, Zhang K, Yu Z, Fung KM, Elgenaid SN, Parrack P, Ali W, Hostetler CA, Milam AN, Nave B, Squires R, Martins PN, Battula NR, Potter S, Pan C, Chen Y, Tang Q. Comprehensive Evaluation of Human Donor Liver Viability with Polarization-Sensitive Optical Coherence Tomography. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.03.31.25321497. [PMID: 40236439 PMCID: PMC11998830 DOI: 10.1101/2025.03.31.25321497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2025]
Abstract
Human liver transplantation is severely constrained by a critical shortage of donor livers, with approximately one quarter of patients on the waiting list dying due to the scarcity of viable organs. Current liver viability assessments, which rely on invasive pathological methods, are hampered by limited sampling from biopsies, particularly in marginal livers from extended criteria donors (ECD) intended to expand the donor pool. Consequently, there is a pressing need for more comprehensive and non-invasive evaluation techniques to meet the escalating demand for liver transplants. In this study, we propose the use of polarization-sensitive optical coherence tomography (PS-OCT) to perform a thorough viability evaluation across the entire surface of donor livers. PS-OCT imaging was conducted on multiple regions, achieving near-complete coverage of the liver surface, and the findings were cross-validated with histopathological evaluations. The analysis of hepatic parameters derived from pathology highlighted tissue heterogeneity. Leveraging machine learning and texture analysis, we quantified hepatic steatosis, fibrosis, inflammation, and necrosis, and established strong correlations (≥ 80%) between PS-OCT quantifications and pathological assessments. PS-OCT offers a non-invasive assessment of liver viability by quantifying hepatic parenchymal parameters across the entire donor liver, significantly complementing current pathological analysis. These results suggest that PS-OCT provides a robust, non-invasive approach to assessing donor liver viability, which could potentially decrease the discard rate of higher risk livers, thereby expanding the donor pool and reducing the inadvertent use of those livers unsuitable for transplantation.
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Zhu Q, Wang Q, Hu X, Dang X, Yu X, Chen L, Hu H. Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis. Diagnostics (Basel) 2025; 15:209. [PMID: 39857093 PMCID: PMC11763746 DOI: 10.3390/diagnostics15020209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 01/10/2025] [Accepted: 01/15/2025] [Indexed: 01/27/2025] Open
Abstract
Objectives: We wished to compare the diagnostic performance of texture analysis (TA) against that of a visual qualitative assessment in identifying early sacroiliitis (nr-axSpA). Methods: A total of 92 participants were retrospectively included at our university hospital institution, comprising 30 controls and 62 patients with axSpA, including 32 with nr-axSpA and 30 with r-axSpA, who underwent MR examination of the sacroiliac joints. MRI at 3T of the lumbar spine and the sacroiliac joint was performed using oblique T1-weighted (W), fluid-sensitive, fat-saturated (Fs) T2WI images. The modified New York criteria for AS were used. Patients were classified into the nr-axSpA group if their digital radiography (DR) and/or CT results within 7 days from the MR examination showed a DR and/or CT grade < 2 for the bilateral sacroiliac joints or a DR and/or CT grade < 3 for the unilateral sacroiliac joint. Patients were classified into the r-axSpA group if their DR and/or CT grade was 2 to 3 for the bilateral sacroiliac joints or their DR and/or CT grade was 3 for the unilateral sacroiliac joint. Patients were considered to have a confirmed diagnosis if their DR or CT grade was 4 for the sacroiliac joints and were thereby excluded. A control group of healthy individuals matched in terms of age and sex to the patients was included in this study. First, two readers independently qualitatively scored the oblique coronal T1WI and FsT2WI non-enhanced sacroiliac joint images. The diagnostic efficacies of the two readers were judged and compared using an assigned Likert score, conducting a Kappa consistency test of the diagnostic results between two readers. Texture analysis models (the T1WI-TA model and the FsT2WI-TA model) were constructed through feature extraction and feature screening. The qualitative and quantitative results were evaluated for their diagnostic performance and compared against a clinical reference standard. Results: The qualitative scores of the two readers could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA and r-axSpA groups (both p < 0.05). Both TA models could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA group and the r-axSpA group (both p < 0.05). There was no significant difference in the differential diagnoses of the two TA models between the healthy controls and the nr-axSpA group (AUC: 0.934 vs. 0.976; p = 0.1838) and between the nr-axSpA and r-axSpA groups (AUC: 0.917 vs. 0.848; p = 0.2592). In terms of distinguishing between the healthy control and nr-axSpA groups, both the TA models were superior to the qualitative scores of the two readers (all p < 0.05). In terms of distinguishing between the nr-axSpA and r-axSpA groups, the T1WI-TA model was superior to the qualitative scores of the two readers (p = 0.023 and p = 0.007), whereas there was no significant difference between the fsT2WI-TA model and the qualitative scores of the two readers (p = 0.134 and p = 0.065). Conclusions: Based on MR imaging, the T1WI-TA and fsT2WI-TA models were highly effective for the early diagnosis of sacroiliac joint arthritis. The T1WI-TA model significantly improved the early diagnostic efficacy for sacroiliac arthritis compared to that of the qualitative scores of the readers, while the efficacy of the fsT2WI-TA model was comparable to that of the readers.
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Affiliation(s)
- Qingqing Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
| | - Qi Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
| | - Xin Dang
- Department of Rheumatology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China;
| | - Xiaojing Yu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
| | - Liye Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China; (Q.Z.); (Q.W.); (X.H.); (X.Y.); (L.C.)
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Chu L, Zeng D, He Y, Dong X, Li Q, Liao X, Zhao T, Chen X, Lei T, Men W, Wang Y, Wang D, Hu M, Pan Z, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y, Li S. Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation. Neuroimage 2024; 302:120893. [PMID: 39426642 DOI: 10.1016/j.neuroimage.2024.120893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 09/15/2024] [Accepted: 10/17/2024] [Indexed: 10/21/2024] Open
Abstract
Brain development is characterized by an increase in structural and functional segregation, which supports the specialization of cognitive processes within the context of network neuroscience. In this study, we investigated age-related changes in morphological segregation using individual Regional Radiomics Similarity Networks (R2SNs) constructed with a longitudinal dataset of 494 T1-weighted MR scans from 309 typically developing children aged 6.2 to 13 years at baseline. Segertation indices were defined as the relative difference in connectivity strengths within and between modules and cacluated at the global, system and local levels. Linear mixed-effect models revealed longitudinal increases in both global and system segregation indices, particularly within the limbic and dorsal attention network, and decreases within the ventral attention network. Superior performance in working memory and inhibitory control was associated with higher system-level segregation indices in default, frontoparietal, ventral attention, somatomotor and subcortical systems, and lower local segregation indices in visual network regions, regardless of age. Furthermore, gene enrichment analysis revealed correlations between age-related changes in local segregation indices and regional expression levels of genes related to developmental processes. These findings provide novel insights into typical brain developmental changes using R2SN-derived segregation indices, offering a valuable tool for understanding human brain structural and cognitive maturation.
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Affiliation(s)
- Lei Chu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xiaoxi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Weiwei Men
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Daoyang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Zhiying Pan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China.
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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Parnianpour P, Steinbach R, Buchholz IJ, Grosskreutz J, Kalra S. T1-weighted MRI texture analysis in amyotrophic lateral sclerosis patients stratified by the D50 progression model. Brain Commun 2024; 6:fcae389. [PMID: 39544700 PMCID: PMC11562117 DOI: 10.1093/braincomms/fcae389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 09/24/2024] [Accepted: 11/03/2024] [Indexed: 11/17/2024] Open
Abstract
Amyotrophic lateral sclerosis, a progressive neurodegenerative disease, presents challenges in predicting individual disease trajectories due to its heterogeneous nature. This study explores the application of texture analysis on T1-weighted MRI in patients with amyotrophic lateral sclerosis, stratified by the D50 disease progression model. The D50 model, which offers a more nuanced representation of disease progression than traditional linear metrics, calculates the sigmoidal curve of functional decline and provides independent quantifications of disease aggressiveness and accumulation. In this research, a representative cohort of 116 patients with amyotrophic lateral sclerosis was studied using the D50 model and texture analysis on MRI images. Texture analysis, a technique used for quantifying voxel intensity patterns in MRI images, was employed to discern alterations in brain tissue associated with amyotrophic lateral sclerosis. This study examined alterations of the texture feature autocorrelation across sub-groups of patients based on disease accumulation, aggressiveness and the first site of onset, as well as in direct regressions with accumulation/aggressiveness. The findings revealed distinct patterns of the texture-derived autocorrelation in grey and white matter, increase in bilateral corticospinal tract, right hippocampus and left temporal pole as well as widespread decrease within motor and extra-motor brain regions, of patients stratified based on their disease accumulation. Autocorrelation alterations in grey and white matter, in clusters within the left cingulate gyrus white matter, brainstem, left cerebellar tonsil grey matter and right inferior fronto-occipital fasciculus, were also negatively associated with disease accumulation in regression analysis. Otherwise, disease aggressiveness correlated with only two small clusters, within the right superior temporal gyrus and right posterior division of the cingulate gyrus white matter. The findings suggest that texture analysis could serve as a potential biomarker for disease stage in amyotrophic lateral sclerosis, with potential for quick assessment based on using T1-weighted images.
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Affiliation(s)
- Pedram Parnianpour
- Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia V6T1Z3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G2S2, Canada
| | - Robert Steinbach
- Department of Neurology, Jena University Hospital, Jena 07747, Germany
| | - Isabelle Jana Buchholz
- Precision Neurology of Neuromuscular Diseases, University of Lübeck, Lübeck 23538, Germany
- Cluster of Excellence of Precision Medicine in Inflammation (PMI), Universities of Lübeck and Kiel, Lübeck 23538, Germany
| | - Julian Grosskreutz
- Precision Neurology of Neuromuscular Diseases, University of Lübeck, Lübeck 23538, Germany
- Cluster of Excellence of Precision Medicine in Inflammation (PMI), Universities of Lübeck and Kiel, Lübeck 23538, Germany
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G2S2, Canada
- Division of Neurology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G2B7, Canada
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Barioni ED, Lopes SLPDC, Silvestre PR, Yasuda CL, Costa ALF. Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions. J Imaging 2024; 10:263. [PMID: 39590727 PMCID: PMC11595357 DOI: 10.3390/jimaging10110263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2024] [Revised: 10/19/2024] [Accepted: 10/20/2024] [Indexed: 11/28/2024] Open
Abstract
This narrative review explores texture analysis as a valuable technique in dentomaxillofacial diagnosis, providing an advanced method for quantification and characterization of different image modalities. The traditional imaging techniques rely primarily on visual assessment, which may overlook subtle variations in tissue structure. In contrast, texture analysis uses sophisticated algorithms to extract quantitative information from imaging data, thus offering deeper insights into the spatial distribution and relationships of pixel intensities. This process identifies unique "texture signatures", serving as markers for accurately characterizing tissue changes or pathological processes. The synergy between texture analysis and radiomics allows radiologists to transcend traditional size-based or semantic descriptors, offering a comprehensive understanding of imaging data. This method enhances diagnostic accuracy, particularly for the assessment of oral and maxillofacial pathologies. The integration of texture analysis with radiomics expands the potential for precise tissue characterization by moving beyond the limitations of human eye evaluations. This article reviews the current trends and methodologies in texture analysis within the field of dentomaxillofacial imaging, highlights its practical applications, and discusses future directions for research and dental clinical practice.
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Affiliation(s)
- Elaine Dinardi Barioni
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 1506-000, SP, Brazil;
| | - Sérgio Lúcio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos 2245-000, SP, Brazil; (S.L.P.d.C.L.); (P.R.S.)
| | - Pedro Ribeiro Silvestre
- Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos 2245-000, SP, Brazil; (S.L.P.d.C.L.); (P.R.S.)
| | - Clarissa Lin Yasuda
- Laboratory of Neuroimaging, Department of Neurology, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil;
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 1506-000, SP, Brazil;
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9
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Huang Y, Zhang H, Chen L, Ding Q, Chen D, Liu G, Zhang X, Huang Q, Zhang D, Weng S. Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma. Front Oncol 2024; 14:1342317. [PMID: 39346735 PMCID: PMC11427235 DOI: 10.3389/fonc.2024.1342317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/23/2024] [Indexed: 10/01/2024] Open
Abstract
Objectives This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies. Methods A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit. Conclusions Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.
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Affiliation(s)
- Yue Huang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Han Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Lingfeng Chen
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Qingzhu Ding
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Dehua Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Guozhong Liu
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiang Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Qiang Huang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Denghan Zhang
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Shangeng Weng
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Clinical Research Center for Hepatobiliary Pancreatic and Gastrointestinal Malignant Tumors Precise Treatment of Fujian Province, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
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10
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Dounavi M, Mak E, Operto G, Muniz‐Terrera G, Bridgeman K, Koychev I, Malhotra P, Naci L, Lawlor B, Su L, Falcon C, Ritchie K, Ritchie CW, Gispert JD, O'Brien JT. Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population. Hum Brain Mapp 2024; 45:e26798. [PMID: 39081128 PMCID: PMC11289425 DOI: 10.1002/hbm.26798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 06/06/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024] Open
Abstract
Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle-aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non-carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross-sectional study, we investigated textural properties of T1-weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT-Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel-based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non-carriers. Textural maps were generated and were subsequently harmonised using voxel-wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non-carriers at midlife and have established associations of textural features with ageing and sex.
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Affiliation(s)
- Maria‐Eleni Dounavi
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Elijah Mak
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
| | - Gregory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Graciela Muniz‐Terrera
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
- Heritage College of Osteopathic MedicineOhio UniversityAthensOhioUSA
| | - Katie Bridgeman
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | | | - Paresh Malhotra
- Division of Brain ScienceImperial College Healthcare NHS TrustUK
| | - Lorina Naci
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Brian Lawlor
- Institute of Neuroscience, Trinity College Dublin, University of DublinIreland
| | - Li Su
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
- Department of NeuroscienceUniversity of SheffieldSheffieldUK
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - Karen Ritchie
- INSERM and University of MontpellierMontpellierFrance
| | - Craig W. Ritchie
- Centre for Dementia PreventionUniversity of EdinburghEdinburghUK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall FoundationBarcelonaSpain
| | - John T. O'Brien
- Department of PsychiatrySchool of Clinical Medicine, University of CambridgeCambridgeUK
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11
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Da Silveira RV, Magalhães TNC, Balthazar MLF, Castellano G. Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis. Exp Brain Res 2024; 242:1947-1955. [PMID: 38910159 DOI: 10.1007/s00221-024-06871-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/07/2024] [Indexed: 06/25/2024]
Abstract
Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.
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Affiliation(s)
- Rafael Vinícius Da Silveira
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil.
| | - Thamires Naela Cardoso Magalhães
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Marcio Luiz Figueredo Balthazar
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
- Department of Neurology and Neuroimaging Laboratory, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Gabriela Castellano
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
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12
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Wang L, Zhou L, Liu S, Zheng Y, Liu Q, Yu M, Lu X, Lei W, Chen G. Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI. Prog Neuropsychopharmacol Biol Psychiatry 2024; 133:111026. [PMID: 38735428 DOI: 10.1016/j.pnpbp.2024.111026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.
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Affiliation(s)
- Li Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Li Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Shengdan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Yurong Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Qianhan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Minglin Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Xiaofei Lu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Wei Lei
- Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
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13
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Sun C, Gong X, Hou L, Yang D, Li Q, Li L, Wang Y. A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients. Front Oncol 2024; 14:1400872. [PMID: 38800371 PMCID: PMC11116775 DOI: 10.3389/fonc.2024.1400872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients. Method A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed. Results The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram's robust consistency and clinical utility. Conclusions The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.
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Affiliation(s)
- Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuantong Gong
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Di Yang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Li
- Department of Ultrasound, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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14
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Parnianpour P, Benatar M, Briemberg H, Dey A, Dionne A, Dupré N, Evans KC, Frayne R, Genge A, Graham SJ, Korngut L, McLaren DG, Seres P, Welsh RC, Wilman A, Zinman L, Kalra S. Mismatch between clinically defined classification of ALS stage and the burden of cerebral pathology. J Neurol 2024; 271:2547-2559. [PMID: 38282082 DOI: 10.1007/s00415-024-12190-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 01/30/2024]
Abstract
This study aimed to investigate the clinical stratification of amyotrophic lateral sclerosis (ALS) patients in relation to in vivo cerebral degeneration. One hundred forty-nine ALS patients and one hundred forty-four healthy controls (HCs) were recruited from the Canadian ALS Neuroimaging Consortium (CALSNIC). Texture analysis was performed on T1-weighted scans to extract the texture feature "autocorrelation" (autoc), an imaging biomarker of cerebral degeneration. Patients were stratified at baseline into early and advanced disease stages based on criteria adapted from ALS clinical trials and the King's College staging system, as well as into slow and fast progressors (disease progression rates, DPR). Patients had increased autoc in the internal capsule. These changes extended beyond the internal capsule in early-stage patients (clinical trial-based criteria), fast progressors, and in advanced-stage patients (King's staging criteria). Longitudinal increases in autoc were observed in the postcentral gyrus, corticospinal tract, posterior cingulate cortex, and putamen; whereas decreases were observed in corpus callosum, caudate, central opercular cortex, and frontotemporal areas. Both longitudinal increases and decreases of autoc were observed in non-overlapping regions within insula and precentral gyrus. Within-criteria comparisons of autoc revealed more pronounced changes at baseline and longitudinally in early- (clinical trial-based criteria) and advanced-stage (King's staging criteria) patients and fast progressors. In summary, comparative patterns of baseline and longitudinal progression in cerebral degeneration are dependent on sub-group selection criteria, with clinical trial-based stratification insufficiently characterizing disease stage based on pathological cerebral burden.
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Affiliation(s)
- Pedram Parnianpour
- Neuroscience and Mental Health Institute, University of Alberta, 562 Heritage Medical Research Centre, 11313-87 Ave, Edmonton, AB, T6G2S2, Canada.
| | - Michael Benatar
- Department of Neurology, University of Miami Miller School of Medicine, Miami, USA
| | - Hannah Briemberg
- Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Avyarthana Dey
- Neuroscience and Mental Health Institute, University of Alberta, 562 Heritage Medical Research Centre, 11313-87 Ave, Edmonton, AB, T6G2S2, Canada
| | - Annie Dionne
- Axe Neurosciences, CHU de Québec-Université Laval, Québec City, QC, Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | - Nicolas Dupré
- Axe Neurosciences, CHU de Québec-Université Laval, Québec City, QC, Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | | | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Angela Genge
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Simon J Graham
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Lawrence Korngut
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | | | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Robert C Welsh
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Alan Wilman
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Lorne Zinman
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta, 562 Heritage Medical Research Centre, 11313-87 Ave, Edmonton, AB, T6G2S2, Canada
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
- Division of Neurology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
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15
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Zhao W, Hu Z, Kazerooni AF, Körzdörfer G, Nittka M, Davatzikos C, Viswanath SE, Wang X, Badve C, Ma D. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Invest Radiol 2024; 59:359-371. [PMID: 37812483 PMCID: PMC10997475 DOI: 10.1097/rli.0000000000001026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. MATERIALS AND METHODS A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. RESULTS First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. CONCLUSIONS The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.
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Affiliation(s)
- Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Zheyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Gan L, Wang L, Liu H, Wang G. Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression. Brain Res 2024; 1830:148819. [PMID: 38403037 DOI: 10.1016/j.brainres.2024.148819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/11/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024]
Abstract
This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.
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Affiliation(s)
- Linfeng Gan
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
| | - Linfeng Wang
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
| | - Hu Liu
- Peking University Health Science Center, Institute of Medical Technology, Beijing 100069, China.
| | - Gang Wang
- School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
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van Staalduinen EK, Matthews R, Khan A, Punn I, Cattell RF, Li H, Franceschi A, Samara GJ, Czerwonka L, Bangiyev L, Duong TQ. Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone. Diagnostics (Basel) 2023; 14:71. [PMID: 38201380 PMCID: PMC10802850 DOI: 10.3390/diagnostics14010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.
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Affiliation(s)
- Eric K. van Staalduinen
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Robert Matthews
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Adam Khan
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Isha Punn
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Renee F. Cattell
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Haifang Li
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ana Franceschi
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Ghassan J. Samara
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lukasz Czerwonka
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Lev Bangiyev
- Stony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA (A.F.); (L.B.)
| | - Tim Q. Duong
- Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA
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Moon SY, Park H, Lee W, Lee S, Lho SK, Kim M, Kim KW, Kwon JS. Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis. Mol Psychiatry 2023; 28:5309-5318. [PMID: 37500824 DOI: 10.1038/s41380-023-02163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/13/2023] [Accepted: 06/23/2023] [Indexed: 07/29/2023]
Abstract
Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix - informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) - were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.
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Affiliation(s)
- Sun Young Moon
- Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Hyungyou Park
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Won Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Subin Lee
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | | | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki Woong Kim
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
- Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Zhang Y, Yang H, Li Z, Gao C, Chen Y, Huang Y, Yue X, Shu C, Wei Y, Cui F, Xu M. A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer. Heliyon 2023; 9:e23242. [PMID: 38144279 PMCID: PMC10746512 DOI: 10.1016/j.heliyon.2023.e23242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023] Open
Abstract
Objective To explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging. Methods A total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Results The radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa. Conclusion The radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.
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Affiliation(s)
- Yongsheng Zhang
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Huan Yang
- Department of Acupuncture and Moxibustion, Community Health Service of Xiaohehushu District, Hangzhou, 310005, China
| | - Zhiping Li
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310006, China
| | - Yin Chen
- Department of Urology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Yasheng Huang
- Department of Urology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Xianjie Yue
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Chang Shu
- Department of Pathology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Yuguo Wei
- Advanced Analytics, Global Medical Service, GE Healthcare, Hangzhou, 310007, China
| | - Feng Cui
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310006, China
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Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz LH, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics (Basel) 2023; 13:3483. [PMID: 37998619 PMCID: PMC10670510 DOI: 10.3390/diagnostics13223483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023] Open
Abstract
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
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Affiliation(s)
- Hayley Higgins
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Abanoub Nakhla
- Department of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The Netherlands;
| | - Andrew Lotfalla
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - David Khalil
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Parth Doshi
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Vandan Thakkar
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Dorsa Shirini
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| | - Maria Bebawy
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Samy Ammari
- Département d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
- ELSAN Département de Radiologie, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Lawrence H. Schwartz
- Department of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Michael Postow
- Melanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Laurent Dercle
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
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21
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Salari E, Elsamaloty H, Ray A, Hadziahmetovic M, Parsai EI. Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning. Am J Clin Oncol 2023; 46:486-495. [PMID: 37580873 PMCID: PMC10589425 DOI: 10.1097/coc.0000000000001036] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
OBJECTIVES Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. METHODS Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS The best performance was achieved using random forest classification with a Gradient filter (AUC=0.910±0.047, accuracy 0.8±0.071, sensitivity=0.796±0.055, specificity=0.922±0.059). For, support vector classification the best result obtains using wavelet_HHH with a high AUC of 0.890±0.89, accuracy of 0.777±0.062, sensitivity=0.701±0.084, and specificity=0.85±0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882±0.051, accuracy of 0.753±0.08, sensitivity=0.717±0.208, and specificity=0.816±0.123. CONCLUSION This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.
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Affiliation(s)
| | | | - Aniruddha Ray
- Department of Physics and Astronomy, Adjunct Faculty
- Department of Radiation Oncology, University of Toledo, Toledo, OH
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Hodgdon T, Thornhill RE, James ND, Melkus G, Beaulé PE, Rakhra KS. MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips. Eur Radiol 2023; 33:8324-8332. [PMID: 37231069 DOI: 10.1007/s00330-023-09748-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/22/2023] [Accepted: 03/26/2023] [Indexed: 05/27/2023]
Abstract
OBJECTIVES To compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model for discriminating between the three hip classes. METHODS A case-control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U tests, and differences in proportions compared using chi-square and Fisher's exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated. RESULTS Sixty-eight subjects (median age 32 (28-40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all p ≤ 0.002) and second-order (11 features, all p ≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all p ≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all p ≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups. CONCLUSION Normal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms. CLINICAL RELEVANCE STATEMENT Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms. KEY POINTS • MRI texture analysis is a technique for extracting quantitative data from routine MRI images. • MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement. • Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.
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Affiliation(s)
- Taryn Hodgdon
- Ottawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada
| | - Rebecca E Thornhill
- Ottawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada
| | - Nick D James
- Department of Information Services, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada
| | - Gerd Melkus
- Ottawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada
| | - Paul E Beaulé
- Department of Orthopaedic Surgery, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada
| | - Kawan S Rakhra
- Ottawa Hospital Research Institute, The Ottawa Hospital - General Campus, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.
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Huang S, Xu F, Zhu W, Xie D, Lou K, Huang D, Hu H. Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter. Clin Radiol 2023; 78:e847-e855. [PMID: 37607844 DOI: 10.1016/j.crad.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 06/20/2023] [Accepted: 07/21/2023] [Indexed: 08/24/2023]
Abstract
AIM To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models. MATERIALS AND METHODS A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC). RESULTS There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility. CONCLUSION The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.
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Affiliation(s)
- S Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, Zhejiang, China
| | - F Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - W Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Shaoxing Second Hospital, Shaoxing, Zhejiang, China
| | - K Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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24
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Huang XW, Ding J, Zheng RR, Ma JY, Cai MT, Powell M, Lin F, Yang YJ, Jin C. An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer. J Med Ultrason (2001) 2023; 50:501-510. [PMID: 37310510 PMCID: PMC10955020 DOI: 10.1007/s10396-023-01331-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/23/2023] [Indexed: 06/14/2023]
Abstract
PURPOSE To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). MATERIALS AND METHODS A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. RESULTS Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). CONCLUSION The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.
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Affiliation(s)
- Xiao-Wan Huang
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Jie Ding
- Department of Ultrasound Imaging, Yueqing Hospital of Wenzhou Medical University, Wenzhou, 325015, People's Republic of China
| | - Ru-Ru Zheng
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Jia-Yao Ma
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Meng-Ting Cai
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Martin Powell
- Nottingham Treatment Centre, Nottingham University Affiliated Hospital, Nottingham, NG7 2FT, UK
| | - Feng Lin
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Yun-Jun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Chu Jin
- Wenzhou Medical University Renji College, University Town, Chashan, Wenzhou, 325000, People's Republic of China.
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Pan T, Su CQ, Tang WT, Lin J, Lu SS, Hong XN. Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas. Acta Radiol 2023; 64:2552-2560. [PMID: 37331987 DOI: 10.1177/02841851231180291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
BACKGROUND Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. PURPOSE To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. MATERIAL AND METHODS This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. RESULTS Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). CONCLUSION Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.
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Affiliation(s)
- Ting Pan
- Department of Radiology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, PR China
| | - Chun-Qiu Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wen-Tian Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Jie Lin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Xun-Ning Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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Lee J, Kim H, Yook S, Kang TY. Identification of document paper using hybrid feature extraction. J Forensic Sci 2023; 68:1808-1815. [PMID: 37420317 DOI: 10.1111/1556-4029.15330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023]
Abstract
Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray-level co-occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.
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Affiliation(s)
- Joong Lee
- Institute of AI and Big Data in Medicine, Yonsei University Wonju College of Medicine, Wonju-si, South Korea
| | - Hongseok Kim
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
| | - Simyub Yook
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
| | - Tae-Yi Kang
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
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27
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Song Y, Zhou G, Zhou Y, Xu Y, Zhang J, Zhang K, He P, Chen M, Liu Y, Sun J, Hu C, Li M, Liao M, Zhang Y, Liao W, Zhou Y. Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study. Hepatol Int 2023; 17:1016-1027. [PMID: 36821045 DOI: 10.1007/s12072-023-10487-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Abstract
OBJECTIVES In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND METHODS A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model. RESULTS The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts. CONCLUSIONS This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.
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Affiliation(s)
- Yangda Song
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Guangyao Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Yucheng Zhou
- Department of General Surgery, Hospital of Integrated TCM and Western Medicine, Southern Medical University, Guangzhou, 510315, China
| | - Yikai Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jing Zhang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Ketao Zhang
- Department of Hepatobiliary Surgery, Shunde Hospital of Southern Medical University, Foshan, 528308, Guangdong, China
| | - Pengyuan He
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, China
| | - Maowei Chen
- Department of Infectious Diseases, Wuming Hospital of Guangxi Medical University, Nanning, 530199, Guangxi, China
| | - Yanping Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Gastroenterology, Second Affiliated Hospital, University of South China, Hengyang, 421001, Hunan, China
| | - Jiarun Sun
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chengguang Hu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
| | - Meng Li
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Minjun Liao
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | | | - Weijia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, China.
| | - Yuanping Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China.
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Duan S, Cao G, Hua Y, Hu J, Zheng Y, Wu F, Xu S, Rong T, Liu B. Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods. World Neurosurg 2023; 175:e823-e831. [PMID: 37059360 DOI: 10.1016/j.wneu.2023.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.
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Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yali Zheng
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Fangfang Wu
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Welton TA, George NM, Ozbay BN, Gentile Polese A, Osborne G, Futia GL, Kushner JK, Kleinschmidt-DeMasters B, Alexander AL, Abosch A, Ojemann S, Restrepo D, Gibson EA. Two-photon microendoscope for label-free imaging in stereotactic neurosurgery. BIOMEDICAL OPTICS EXPRESS 2023; 14:3705-3725. [PMID: 37497482 PMCID: PMC10368057 DOI: 10.1364/boe.492552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 07/28/2023]
Abstract
We demonstrate a gradient refractive index (GRIN) microendoscope with an outer diameter of ∼1.2 mm and a length of ∼186 mm that can fit into a stereotactic surgical cannula. Two photon imaging at an excitation wavelength of 900 nm showed a field of view of ∼180 microns and a lateral and axial resolution of 0.86 microns and 9.6 microns respectively. The microendoscope was tested by imaging autofluorescence and second harmonic generation (SHG) in label-free human brain tissue. Furthermore, preliminary image analysis indicates that image classification models can predict if an image is from the subthalamic nucleus or the surrounding tissue using conventional, bench-top two-photon autofluorescence.
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Affiliation(s)
- Tarah A. Welton
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas M. George
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Baris N. Ozbay
- Intelligent Imaging Innovations, Denver, Colorado, 80216, USA
| | - Arianna Gentile Polese
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Gregory Osborne
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Gregory L. Futia
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - J. Keenan Kushner
- Neuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Bette Kleinschmidt-DeMasters
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Allyson L. Alexander
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Division of Pediatric Neurosurgery, Children’s Hospital Colorado, Aurora CO 80045, USA
| | - Aviva Abosch
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Steven Ojemann
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Diego Restrepo
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Emily A. Gibson
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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Wei Q, Yuan W, Jia Z, Chen J, Li L, Yan Z, Liao Y, Mao L, Hu S, Liu X, Chen W. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. Abdom Radiol (NY) 2023; 48:458-470. [PMID: 36460837 DOI: 10.1007/s00261-022-03731-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVES Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively. METHODS A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis. RESULTS The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively. CONCLUSION The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Wenjing Yuan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Ziqi Jia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Jialiang Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Ling Li
- Department of Radiology, The Second People's Hospital of Shaanxi Province, Xi'an, 710000, Shaanxi province, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Yuting Liao
- GE Healthcare, Guangzhou, 510623, Guangdong Province, China
| | - Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Shaowei Hu
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Caruso M, Stanzione A, Prinster A, Pizzuti LM, Brunetti A, Maurea S, Mainenti PP. Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases. World J Gastroenterol 2023; 29:521-535. [PMID: 36688023 PMCID: PMC9850941 DOI: 10.3748/wjg.v29.i3.521] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/25/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023] Open
Abstract
In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.
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Affiliation(s)
- Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Anna Prinster
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Laura Micol Pizzuti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
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Bhattacharya S, Bennet L, Davidson JO, Unsworth CP. Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training. PLoS One 2022; 17:e0278874. [PMID: 36512546 PMCID: PMC9746996 DOI: 10.1371/journal.pone.0278874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.
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Affiliation(s)
- Saheli Bhattacharya
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Laura Bennet
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Joanne O. Davidson
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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Muacevic A, Adler JR, Issa M, Ali O, Noureldin K, Gaber A, Mahgoub A, Ahmed M, Yousif M, Zeinaldine A. Textural Analysis as a Predictive Biomarker in Rectal Cancer. Cureus 2022; 14:e32241. [PMID: 36620843 PMCID: PMC9813797 DOI: 10.7759/cureus.32241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer (CRC) is a common deadly cancer. Early detection and accurate staging of CRC enhance good prognosis and better treatment outcomes. Rectal cancer staging is the cornerstone for selecting the best treatment approach. The standard gold method for rectal cancer staging is pelvic MRI. After staging, combining surgery and chemoradiation is the standard management aiming for a curative outcome. Textural analysis (TA) is a radiomic process that quantifies lesions' heterogenicity by measuring pixel distribution in digital imaging. MRI textural analysis (MRTA) of rectal cancer images is growing in current literature as a future predictor of outcomes of rectal cancer management, such as pathological response to neoadjuvant chemoradiotherapy (NCRT), survival, and tumour recurrence. MRTA techniques could validate alternative approaches in rectal cancer treatment, such as the wait-and-watch (W&W) approach in pathologically complete responders (pCR) following NCRT. We consider this a significant step towards implementing precision management in rectal cancer. In this narrative review, we summarize the current knowledge regarding the potential role of TA in rectal cancer management in predicting the prognosis and clinical outcomes, as well as aim to delineate the challenges which obstruct the implementing of this new modality in clinical practice.
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Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review. Cancers (Basel) 2022; 14:cancers14225608. [PMID: 36428701 PMCID: PMC9688156 DOI: 10.3390/cancers14225608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/02/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake.
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Lam LHT, Do DT, Diep DTN, Nguyet DLN, Truong QD, Tri TT, Thanh HN, Le NQK. Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning. NMR IN BIOMEDICINE 2022; 35:e4792. [PMID: 35767281 DOI: 10.1002/nbm.4792] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 05/22/2023]
Abstract
In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three-subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.
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Affiliation(s)
- Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Children's Hospital 2, Ho Chi Minh City, Vietnam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Doan Thi Ngoc Diep
- Department of Pediatrics, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | | | | | - Huynh Ngoc Thanh
- Department of Pediatrics, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan
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Diagnostic utility of magnetic resonance imaging texture analysis in suppurative osteomyelitis of the mandible. Oral Radiol 2022; 38:601-609. [PMID: 35157182 DOI: 10.1007/s11282-022-00595-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/24/2022] [Indexed: 10/19/2022]
Abstract
PURPOSE This study aimed to determine the diagnostic utility of magnetic resonance imaging (MRI) texture analysis for evaluating mandibular suppurative osteomyelitis (OM). MATERIALS AND METHODS In this retrospective cohort study, we analyzed the records of 50 patients with and without OM who underwent MRI between April 2019 and March 2021. The presence or absence of OM served as a predictor variable. The outcome variables were the texture features of the region of interest, which were analyzed. Quantitative parameters based on histogram features (90th percentile) and gray-level co-occurrence matrix (GLCM) features (Sum Averg) were calculated using short-tau inversion-recovery data with a region of interest. These six features out of 279 parameters were selected using Fisher, probability of error, and average correlation coefficient methods in MaZda. For the analysis of trivariate statistics, the post-Mann-Whitney test of the Kruskal-Wallis test with Bonferroni adjustment was used, and the p value was set to 0.05. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic effect of texture function to distinguish between acute and chronic diseases. RESULTS One histogram feature and five GLCM features showed differences among the non-OM patients, acute OM patients, and chronic OM patients (p < 0.05). The ROC analysis revealed a high area under the curve ranging from 0.91 to 0.96 for six texture features. CONCLUSION The six texture features of the mandibular bone marrow demonstrated differences among patients without and with acute and chronic OM. MRI texture analysis may facilitate accurate assessment of the mandibular OM stage.
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Veluppal A, sadhukhan D, gopinath V, swaminathan R. Differentiation of Alzheimer conditions in brain MR images using bidimensional multiscale entropy-based texture analysis of lateral ventricles. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. Sci Rep 2022; 12:13412. [PMID: 35927323 PMCID: PMC9352871 DOI: 10.1038/s41598-022-17707-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/29/2022] [Indexed: 11/30/2022] Open
Abstract
O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.
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Zhai H, Liu Z, Wu S, Cao Z, Xu Y, Lv Y. Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction. Front Neurosci 2022; 16:923708. [PMID: 35937879 PMCID: PMC9353395 DOI: 10.3389/fnins.2022.923708] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/30/2022] [Indexed: 01/31/2023] Open
Abstract
Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (n = 44) and non-HT group (n = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (p < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.
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Affiliation(s)
- Heng Zhai
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhijun Liu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziqin Cao
- Department of Chemistry, Emory University, Atlanta, GA, United States
| | - Yan Xu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Yan Xu,
| | - Yinzhang Lv
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Yinzhang Lv,
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Souza SAS, Reis LO, Alves AFF, Silva LC, Medeiros MCK, Andrade DL, Billis A, Amaro JL, Martins DL, Trindade AP, Miranda JRA, Pina DR. Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue. Phys Eng Sci Med 2022; 45:525-535. [PMID: 35325377 DOI: 10.1007/s13246-022-01118-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 03/09/2022] [Indexed: 10/18/2022]
Abstract
Several studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.
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Affiliation(s)
- Sérgio Augusto Santana Souza
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | - Leonardo Oliveira Reis
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Allan Felipe Fattori Alves
- Botucatu Medical School, Clinics Hospital, Medical Physics and Radioprotection Nucleus, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP: 18618687, Brazil
| | - Letícia Cotinguiba Silva
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | | | - Danilo Leite Andrade
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Athanase Billis
- Department of Anatomic Pathology and Urology, School of Medical Sciences, State University of Campinas (Unicamp), Campinas, Brazil
| | - João Luiz Amaro
- Department of Urology, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | | | - André Petean Trindade
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil
| | - José Ricardo Arruda Miranda
- Institute of Bioscience, São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 8618-689, Brazil
| | - Diana Rodrigues Pina
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil.
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Huang S, Shi K, Zhang Y, Yan WF, Guo YK, Li Y, Yang ZG. Texture analysis of T2-weighted cardiovascular magnetic resonance imaging to discriminate between cardiac amyloidosis and hypertrophic cardiomyopathy. BMC Cardiovasc Disord 2022; 22:235. [PMID: 35597906 PMCID: PMC9124433 DOI: 10.1186/s12872-022-02671-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 05/12/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To elucidate the value of texture analysis (TA) in detecting and differentiating myocardial tissue alterations on T2-weighted CMR (cardiovascular magnetic resonance imaging) in patients with cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM). METHODS In this retrospective study, 100 CA (58.5 ± 10.7 years; 41 (41%) females) and 217 HCM (50.7 ± 14.8 years, 101 (46.5%) females) patients who underwent CMR scans were included. Regions of interest for TA were delineated by two radiologists independently on T2-weighted imaging (T2WI). Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning algorithms, and correlation analyses were performed to select features. Both the CA and HCM groups were randomly divided into a training dataset and a testing dataset (7:3). After the TA model was established in the training set, the diagnostic performance of the model was validated in the testing set and further validated in a subgroup of patients with similar hypertrophy. RESULTS The 7 independent texture features provided, in combination, a diagnostic accuracy of 86.0% (AUC = 0.915; 95% CI 0.879-0.951) in the training dataset and 79.2% (AUC = 0.842; 95% CI 0.759-0.924) in the testing dataset. The differential diagnostic accuracy in the similar hypertrophy subgroup was 82.2% (AUC = 0.864, 95% CI 0.805-0.922). The significance of the difference between the AUCs of the TA model and late gadolinium enhancement (LGE) was verified by Delong's test (p = 0.898). All seven texture features showed significant differences between CA and HCM (all p < 0.001). CONCLUSIONS Our study demonstrated that texture analysis based on T2-weighted images could feasibly differentiate CA from HCM, even in patients with similar hypertrophy. The selected final texture features could achieve a comparable diagnostic capacity to the quantification of LGE. Trial registration Since this study is a retrospective observational study and no intervention had been involved, trial registration is waived.
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Affiliation(s)
- Shan Huang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Ke Shi
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Yi Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Wei-Feng Yan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Ying-Kun Guo
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yuan Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| | - Zhi-Gang Yang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
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Dounavi ME, Low A, Muniz-Terrera G, Ritchie K, Ritchie CW, Su L, Markus HS, O’Brien JT. Fluid-attenuated inversion recovery magnetic resonance imaging textural features as sensitive markers of white matter damage in midlife adults. Brain Commun 2022; 4:fcac116. [PMID: 35611309 PMCID: PMC9123845 DOI: 10.1093/braincomms/fcac116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/28/2022] [Accepted: 05/04/2022] [Indexed: 11/18/2022] Open
Abstract
White matter hyperintensities are common radiological findings in ageing and a typical manifestation of cerebral small vessel disease. White matter hyperintensity burden is evaluated by quantifying their volume; however, subtle changes in the white matter may not be captured by white matter hyperintensity volumetry. In this cross-sectional study, we investigated whether magnetic resonance imaging texture of both white matter hyperintensities and normal appearing white matter was associated with reaction time, white matter hyperintensity volume and dementia risk in a midlife cognitively normal population. Data from 183 cognitively healthy midlife adults from the PREVENT-Dementia study (mean age 51.9 ± 5.4; 70% females) were analysed. White matter hyperintensities were segmented from 3 Tesla fluid-attenuated inversion recovery scans using a semi-automated approach. The fluid-attenuated inversion recovery images were bias field corrected and textural features (intensity mean and standard deviation, contrast, energy, entropy, homogeneity) were calculated in white matter hyperintensities and normal appearing white matter based on generated textural maps. Textural features were analysed for associations with white matter hyperintensity volume, reaction time and the Cardiovascular Risk Factors, Aging and Dementia risk score using linear regression models adjusting for age and sex. The extent of normal appearing white matter surrounding white matter hyperintensities demonstrating similar textural associations to white matter hyperintensities was further investigated by defining layers surrounding white matter hyperintensities at increments of 0.86 mm thickness. Lower mean intensity within white matter hyperintensities was a significant predictor of longer reaction time (t = −3.77, P < 0.01). White matter hyperintensity volume was predicted by textural features within white matter hyperintensities and normal appearing white matter, albeit in opposite directions. A white matter area extending 2.5 – 3.5 mm further from the white matter hyperintensities demonstrated similar associations. White matter hyperintensity volume was not related to reaction time, although interaction analysis revealed that participants with high white matter hyperintensity burden and less homogeneous white matter hyperintensity texture demonstrated slower reaction time. Higher Cardiovascular Risk Factors, Aging, and Dementia score was associated with a heterogeneous normal appearing white matter intensity pattern. Overall, greater homogeneity within white matter hyperintensities and a more heterogeneous normal appearing white matter intensity profile were connected to a higher white matter hyperintensity burden, while heterogeneous intensity was related to prolonged reaction time (white matter hyperintensities of larger volume) and dementia risk (normal appearing white matter). Our results suggest that the quantified textural measures extracted from widely used clinical scans, might capture underlying microstructural damage and might be more sensitive to early pathological changes compared to white matter hyperintensity volumetry.
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Affiliation(s)
- Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, United Kingdom
| | - Audrey Low
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, United Kingdom
| | | | - Karen Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, United Kingdom
- INM, Univ Montpellier, INSERM, Montpellier, France
| | - Craig W. Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, United Kingdom
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, United Kingdom
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - John T. O’Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, United Kingdom
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Jiang W, Wang S, Wan J, Zheng J, Dong X, Liu Z, Wang G, Xu S, Xiao W, Gao Y, Zhuo S, Yan J. Association of the Collagen Signature with Pathological Complete Response in Rectal Cancer Patients. Cancer Sci 2022; 113:2409-2424. [PMID: 35485874 PMCID: PMC9277261 DOI: 10.1111/cas.15385] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/20/2022] [Accepted: 04/24/2022] [Indexed: 11/28/2022] Open
Abstract
Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.
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Affiliation(s)
- Wei Jiang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China.,School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Shijie Wang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jinliang Wan
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jixiang Zheng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xiaoyu Dong
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Zhangyuanzhu Liu
- Department of Hepatobiliary and Pancreatic Surgery, Guangdong Provincial Hospital of Traditional Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Guangxing Wang
- School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Shuoyu Xu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China
| | - Weiwei Xiao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, 510060, China
| | - Yuanhong Gao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, 510060, China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, Fujian, 361021, China
| | - Jun Yan
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, China
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Popecki P, Jurczyszyn K, Ziętek M, Kozakiewicz M. Texture Analysis in Diagnosing Skin Pigmented Lesions in Normal and Polarized Light-A Preliminary Report. J Clin Med 2022; 11:jcm11092505. [PMID: 35566634 PMCID: PMC9101611 DOI: 10.3390/jcm11092505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 12/04/2022] Open
Abstract
The differential diagnosis of benign nevi (BN), dysplastic nevi (DN), and melanomas (MM) represents a considerable clinical problem. These lesions are similar in clinical examination but have different prognoses and therapeutic management techniques. A texture analysis (TA) is a mathematical and statistical analysis of pixel patterns of a digital image. This study aims to demonstrate the relationship between the TA of digital images of pigmented lesions under polarized and non-polarized light and their histopathological diagnosis. Ninety pigmented lesions of 76 patients were included in this study. We obtained 166 regions of interest (ROI) images for MM, 166 for DN, and 166 for BN. The pictures were taken under polarized and non-polarized light. Selected image texture features (entropy and difference entropy and long-run emphasis) of ROIs were calculated. Those three equations were used to construct the texture index (TI) and bone index (BI). All of the presented features distinguish melanomas, benign and dysplastic lesions under polarized light very well. In non-polarized images, only the long-run emphasis moment and both indices effectively differentiated nevi from melanomas. TA is an objective method of assessing pigmented lesions and can be used in automatic diagnostic systems.
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Affiliation(s)
- Paweł Popecki
- Department of Oral Surgery, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland;
| | - Kamil Jurczyszyn
- Department of Oral Surgery, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland;
- Correspondence: ; Tel.: +48-71-784-04-23
| | - Marcin Ziętek
- Department of Oncology, Wroclaw Medical University, Plac Hirszfelda 12, 53-413 Wroclaw, Poland;
- Department of Surgical Oncology, Wroclaw Comprehensive Cancer Center, Plac Hirszfelda 12, 53-413 Wroclaw, Poland
| | - Marcin Kozakiewicz
- Department of Maxillofacial Surgery, Medical University of Lodz, 113 S. Zeromski Street, 90-549 Lodz, Poland;
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Deep G, Kaur J, Singh SP, Nayak SR, Kumar M, Kautish S. MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9505229. [PMID: 35449840 PMCID: PMC9017451 DOI: 10.1155/2022/9505229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 02/21/2022] [Indexed: 02/08/2023]
Abstract
Image texture analysis is a dynamic area of research in computer vision and image processing, with applications ranging from medical image analysis to image segmentation to content-based image retrieval and beyond. "Quinary encoding on mesh patterns (MeQryEP)" is a new approach to extracting texture features for indexing and retrieval of biomedical images, which is implemented in this work. An extension of the previous study, this research investigates the use of local quinary patterns (LQP) on mesh patterns in three different orientations. To encode the gray scale relationship between the central pixel and its surrounding neighbors in a two-dimensional (2D) local region of an image, binary and nonbinary coding, such as local binary patterns (LBP), local ternary patterns (LTP), and LQP, are used, while the proposed strategy uses three selected directions of mesh patterns to encode the gray scale relationship between the surrounding neighbors for a given center pixel in a 2D image. An innovative aspect of the proposed method is that it makes use of mesh image structure quinary pattern features to encode additional spatial structure information, resulting in better retrieval. On three different kinds of benchmark biomedical data sets, analyses have been completed to assess the viability of MeQryEP. LIDC-IDRI-CT and VIA/I-ELCAP-CT are the lung image databases based on computed tomography (CT), while OASIS-MRI is a brain database based on magnetic resonance imaging (MRI). This method outperforms state-of-the-art texture extraction methods, such as LBP, LQEP, LTP, LMeP, LMeTerP, DLTerQEP, LQEQryP, and so on in terms of average retrieval precision (ARP) and average retrieval rate (ARR).
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Affiliation(s)
- G. Deep
- Chandigarh Engineering College Landran, Mohali, India
| | - J. Kaur
- Chandigarh Engineering College Landran, Mohali, India
| | | | - Soumya Ranjan Nayak
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Manoj Kumar
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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Fujita S, Hagiwara A, Yasaka K, Akai H, Kunimatsu A, Kiryu S, Fukunaga I, Kato S, Akashi T, Kamagata K, Wada A, Abe O, Aoki S. Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features. Eur Radiol 2022; 32:4791-4800. [PMID: 35304637 PMCID: PMC9213334 DOI: 10.1007/s00330-022-08555-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/23/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022]
Abstract
Objectives We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans. Methods Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases. Results The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps. Conclusion MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries. Key Points • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08555-3.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan. .,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852, Hatakeda, Narita, Chiba, 286-8520, Japan
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Shimpei Kato
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
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Booth TC, Wiegers EC, Warnert EAH, Schmainda KM, Riemer F, Nechifor RE, Keil VC, Hangel G, Figueiredo P, Álvarez-Torres MDM, Henriksen OM. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics. Front Oncol 2022; 11:811425. [PMID: 35340697 PMCID: PMC8948428 DOI: 10.3389/fonc.2021.811425] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023] Open
Abstract
Objective To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging). Results The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application. Conclusion Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ruben E. Nechifor
- Department of Clinical Psychology and Psychotherapy International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Gilbert Hangel
- Department of Neurosurgery & High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Patrícia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Otto M. Henriksen
- Department of Clinical Physiology, Nuclear medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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MRI histogram analysis of optic nerves in children with type 1 neurofibromatosis. JOURNAL OF SURGERY AND MEDICINE 2022. [DOI: 10.28982/josam.990310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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50
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Jeong S, Lee EJ, Kim YH, Woo JC, Ryu OW, Kwon M, Kwon SU, Kim JS, Kang DW. Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients. J Stroke 2022; 24:108-117. [PMID: 35135064 PMCID: PMC8829479 DOI: 10.5853/jos.2021.02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/16/2021] [Accepted: 10/05/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND AND PURPOSE This study aimed to investigate the applicability of deep learning (DL) model using diffusion-weighted imaging (DWI) data to predict the severity of aphasia at an early stage in acute stroke patients. METHODS We retrospectively analyzed consecutive patients with aphasia caused by acute ischemic stroke in the left middle cerebral artery territory, who visited Asan Medical Center between 2011 and 2013. To implement the DL model to predict the severity of post-stroke aphasia, we designed a deep feed-forward network and utilized the lesion occupying ratio from DWI data and established clinical variables to estimate the aphasia quotient (AQ) score (range, 0 to 100) of the Korean version of the Western Aphasia Battery. To evaluate the performance of the DL model, we analyzed Cohen's weighted kappa with linear weights for the categorized AQ score (0-25, very severe; 26-50, severe; 51-75, moderate; ≥76, mild) and Pearson's correlation coefficient for continuous values. RESULTS We identified 225 post-stroke aphasia patients, of whom 176 were included and analyzed. For the categorized AQ score, Cohen's weighted kappa coefficient was 0.59 (95% confidence interval [CI], 0.42 to 0.76; P<0.001). For continuous AQ score, the correlation coefficient between true AQ scores and model-estimated values was 0.72 (95% CI, 0.55 to 0.83; P<0.001). CONCLUSIONS DL approaches using DWI data may be feasible and useful for estimating the severity of aphasia in the early stage of stroke.
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Affiliation(s)
- Soo Jeong
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun-Jae Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | | | - Jin Cheol Woo
- Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - On-Wha Ryu
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Miseon Kwon
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sun U Kwon
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jong S. Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Wha Kang
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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