|
1
|
Kawata N, Iwao Y, Matsuura Y, Higashide T, Okamoto T, Sekiguchi Y, Nagayoshi M, Takiguchi Y, Suzuki T, Haneishi H. Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19. Jpn J Radiol 2024:10.1007/s11604-024-01699-w. [PMID: 39585556 DOI: 10.1007/s11604-024-01699-w] [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: 05/20/2024] [Accepted: 11/02/2024] [Indexed: 11/26/2024]
Abstract
PURPOSE Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19. MATERIALS AND METHODS We retrospectively enrolled 505 patients with COVID-19 for whom the clinical parameters (patient background, clinical symptoms, and blood test results) upon admission were available and chest CT imaging was performed. Subject datasets (n = 505) were allocated for training (n = 403), and the remaining (n = 102) were reserved for evaluation. The image underwent variational autoencoder (VAE) encoding, resulting in latent vectors. The information consisting of initial clinical parameters and radiomic features were formatted as a table data encoder. Initial and follow-up latent vectors and the initial table data encoders were utilized for training the diffusion model. The evaluation data were used to generate prognostic images. Then, similarity of the prognostic images (generated images) and the follow-up images (real images) was evaluated by zero-mean normalized cross-correlation (ZNCC), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Visual assessment was also performed using a numerical rating scale. RESULTS Prognostic chest CT images were generated using the diffusion model. Image similarity showed reasonable values of 0.973 ± 0.028 for the ZNCC, 24.48 ± 3.46 for the PSNR, and 0.844 ± 0.075 for the SSIM. Visual evaluation of the images by two pulmonologists and one radiologist yielded a reasonable mean score. CONCLUSIONS The similarity and validity of generated predictive images for the course of COVID-19-associated pneumonia using a diffusion model were reasonable. The generation of prognostic images may suggest potential utility for early prediction of the clinical course in COVID-19-associated pneumonia and other respiratory diseases.
Collapse
Affiliation(s)
- Naoko Kawata
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8677, Japan.
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan.
| | - Yuma Iwao
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-Cho, Inage-Ku, Chiba-Shi, Chiba, 263-8522, Japan
- Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Yukiko Matsuura
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-Cho, Chuo-Ku, Chiba-Shi, Chiba, 260-0852, Japan
| | - Takashi Higashide
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8677, Japan
- Department of Radiology, Japanese Red Cross Narita Hospital, 90-1, Iida-Cho, Narita-Shi, Chiba, 286-8523, Japan
| | - Takayuki Okamoto
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-Cho, Inage-Ku, Chiba-Shi, Chiba, 263-8522, Japan
| | - Yuki Sekiguchi
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Masaru Nagayoshi
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-Cho, Chuo-Ku, Chiba-Shi, Chiba, 260-0852, Japan
| | - Yasuo Takiguchi
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-Cho, Chuo-Ku, Chiba-Shi, Chiba, 260-0852, Japan
| | - Takuji Suzuki
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8677, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-Cho, Inage-Ku, Chiba-Shi, Chiba, 263-8522, Japan
| |
Collapse
|
|
2
|
Zhao ZC, Liu JX, Sun LL. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artif Intell Med Imaging 2024; 5:93993. [DOI: 10.35711/aimi.v5.i1.93993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 08/27/2024] [Accepted: 09/05/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND The presence of perineural invasion (PNI) in patients with rectal cancer (RC) is associated with significantly poorer outcomes. However, traditional diagnostic modalities have many limitations.
AIM To develop a deep learning radiomics stacking nomogram model to predict preoperative PNI status in patients with RC.
METHODS We recruited 303 RC patients and separated them into the training (n = 242) and test (n = 61) datasets on an 8: 2 scale. A substantial number of deep learning and hand-crafted radiomics features of primary tumors were extracted from the arterial and venous phases of computed tomography (CT) images. Four machine learning models were used to predict PNI status in RC patients: support vector machine, k-nearest neighbor, logistic regression, and multilayer perceptron. The stacking nomogram was created by combining optimal machine learning models for the arterial and venous phases with predicting clinical variables.
RESULTS With an area under the curve (AUC) of 0.964 [95% confidence interval (CI): 0.944-0.983] in the training dataset and an AUC of 0.955 (95%CI: 0.900-0.999) in the test dataset, the stacking nomogram demonstrated strong performance in predicting PNI status. In the training dataset, the AUC of the stacking nomogram was greater than that of the arterial support vector machine (ASVM), venous SVM, and CT-T stage models (P < 0.05). Although the AUC of the stacking nomogram was greater than that of the ASVM in the test dataset, the difference was not particularly noticeable (P = 0.05137).
CONCLUSION The developed deep learning radiomics stacking nomogram was effective in predicting preoperative PNI status in RC patients.
Collapse
Affiliation(s)
- Zhi-Chun Zhao
- Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Jia-Xuan Liu
- Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
| | - Ling-Ling Sun
- Department of Radiology, The fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
| |
Collapse
|
|
3
|
Iwao Y, Kawata N, Sekiguchi Y, Haneishi H. Nonrigid registration method for longitudinal chest CT images in COVID-19. Heliyon 2024; 10:e37272. [PMID: 39286087 PMCID: PMC11403531 DOI: 10.1016/j.heliyon.2024.e37272] [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: 03/15/2023] [Revised: 08/22/2024] [Accepted: 08/30/2024] [Indexed: 09/19/2024] Open
Abstract
Rationale and objectives To analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. Materials and methods First, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. Results The proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. Conclusion The developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.
Collapse
Affiliation(s)
- Yuma Iwao
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
- Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan
| | - Naoko Kawata
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shii, Chiba, 260-8677, Japan
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
- Medical Mycology Research Center (MMRC), Chiba University, Japan
| | - Yuki Sekiguchi
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
| |
Collapse
|
|
4
|
Vásquez-Venegas C, Sotomayor CG, Ramos B, Castañeda V, Pereira G, Cabrera-Vives G, Härtel S. Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients. J Clin Med 2024; 13:5231. [PMID: 39274444 PMCID: PMC11396404 DOI: 10.3390/jcm13175231] [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: 06/18/2024] [Revised: 08/02/2024] [Accepted: 09/02/2024] [Indexed: 09/16/2024] Open
Abstract
Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.
Collapse
Affiliation(s)
- Constanza Vásquez-Venegas
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Camilo G Sotomayor
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Baltasar Ramos
- School of Medicine, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Víctor Castañeda
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Gonzalo Pereira
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Guillermo Cabrera-Vives
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
| | - Steffen Härtel
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- National Center for Health Information Systems, Santiago 8380453, Chile
- Center of Mathematical Modelling, University of Chile, Santiago 8380453, Chile
| |
Collapse
|
|
5
|
Wang H, Zhao L, Yang Y, Wang F, Ding H, He L, Han J, Chen X. Chest Computed Tomography Radiomics for Determining Macrolide Resistance-Associated Gene Mutation Status in Children with Mycoplasma pneumoniae Pneumonia: A Two-Center Study. Acad Radiol 2024; 31:3774-3782. [PMID: 38772798 DOI: 10.1016/j.acra.2024.05.008] [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: 04/06/2024] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES The mutations in the 23S ribosomal RNA (rRNA) gene are associated with an increase in resistance to macrolides in children with Mycoplasma pneumoniae pneumonia (MPP). This study aimed to develop and validate a chest computed tomography (CT) radiomics model for determining macrolide resistance-associated gene mutation status in MPP. MATERIALS AND METHODS A total of 258 MPP patients were retrospectively included from two institutions (training set: 194 patients from the first institution; external test set: 64 patients from the second). The 23S rRNA gene mutation status was tested by nasopharyngeal swab polymerase chain reaction. Radiomics features were extracted from chest CT images of pulmonary lesions segmented with semi-automatic delineation. Subsequently, radiomics feature reduction was applied to identify the most relevant features. Logistic regression and random forest algorithms were employed to establish the radiomics models, which were five-fold cross-validated in the training set and validated in the external test set. RESULTS The radiomics feature selection resulted in eight features. After five-fold cross-validation in the training set, the mean areas under the receiver operating characteristic curve (AUCs) of the logistic regression and random forest models were 0.868 (95% confidence interval (CI): 0.813-0.923) and 0.941 (95% CI: 0.907-0.975), respectively. In the external test set, the corresponding AUCs were 0.855 (95% CI: 0.758-0.952) and 0.815 (95% CI: 0.705-0.925). CONCLUSION Chest CT radiomics is a promising diagnostic tool for determining macrolide resistance gene mutation status in MPP. AVAILABILITY OF DATA AND MATERIAL The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Collapse
Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Leilei Zhao
- Department of Radiology, Jinan Children's Hospital (Qilu Children's Hospital of Shandong University), Shandong 250000, China
| | - Yanlin Yang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
| | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Jie Han
- Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
| |
Collapse
|
|
6
|
Dong S, Fu A, Liu J. Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose ( 18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning. Quant Imaging Med Surg 2024; 14:4723-4734. [PMID: 39022286 PMCID: PMC11250303 DOI: 10.21037/qims-24-100] [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: 01/16/2024] [Accepted: 05/11/2024] [Indexed: 07/20/2024]
Abstract
Background For patient management and prognosis, accurate assessment of mediastinal lymph node (LN) status is essential. This study aimed to use machine learning approaches to assess the status of confusing LNs in the mediastinum using positron emission tomography/computed tomography (PET/CT) images; the results were then compared with the diagnostic conclusions of nuclear medicine physicians. Methods A total of 509 confusing mediastinal LNs that had undergone pathological assessment or follow-up from 320 patients from three centres were retrospectively included in the study. LNs from centres I and II were randomised into a training cohort (N=324) and an internal validation cohort (N=81), while those from centre III patients formed an external validation cohort (N=104). Various parameters measured from PET and CT images and extracted radiomics and deep learning features were used to construct PET/CT-parameter, radiomics, and deep learning models, respectively. Model performance was compared with the diagnostic results of nuclear medicine physicians using the area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). Results The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) incorporating radiomic features showed AUCs of 92.2% [95% confidence interval (CI), 0.890-0.953], 84.6% (95% CI, 0.761-0.930) and 84.6% (95% CI, 0.770-0.922) across the three cohorts. It significantly outperformed the deep learning model, the parametric PET/CT model and the physician's diagnosis. DCA demonstrated the clinical usefulness of the GBDT-LR model. Conclusions The presented GBDT-LR model performed well in evaluating confusing mediastinal LNs in both internal and external validation sets. It not only crossed radiometric features but also avoided overfitting.
Collapse
Affiliation(s)
- Siqin Dong
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School, Southeast University, Nanjing, China
| | - Ao Fu
- Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China
| | - Jiacheng Liu
- Department of Nuclear Medicine, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| |
Collapse
|
|
7
|
Zhu B, Nie Y, Zheng S, Lin S, Li Z, Wu W. CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones. Urolithiasis 2024; 52:91. [PMID: 38878124 DOI: 10.1007/s00240-024-01593-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/06/2024] [Indexed: 12/17/2024]
Abstract
Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were randomly allocated to training and validation sets at a 7:3 ratio. Radiomic features were extracted using 3Dslicer software. The least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features from the 107 extracted features, and logistic regression, decision tree, AdaBoost, and support vector machine (SVM) models were subsequently used to construct radiomic feature prediction models. Among these, the logistic regression algorithm demonstrated the best predictive performance and stability. The area under the curve (AUC) of the logistic regression model based on radiomic features was 0.858 in the training cohort and 0.806 in the validation cohort. Furthermore, univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for kidney stones, which were gender and body mass index (BMI). Combining these independent risk factors improved the predictive performance of the model, with AUC values of 0.860 in the training cohort and 0.814 in the validation cohort. Clinical decision curve analysis (DCA) indicated that the radiomic model provided clinical benefit when the probability ranged from 0.2 to 1.0. The radiomic model has a good ability to screen high-risk patients with kidney stones, facilitating early intervention in kidney stone cases and improving patient prognosis.
Collapse
Grants
- 82070719 National Natural Science Foundation of China
- 82070719 National Natural Science Foundation of China
- 82070719 National Natural Science Foundation of China
- 82070719 National Natural Science Foundation of China
- 82070719 National Natural Science Foundation of China
- 82070719 National Natural Science Foundation of China
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2020A151501198 Natural Science Foundation of Guangdong Province
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 2018KZDXM056 Research Project of Education Department of Guangdong province,China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 201716007 Special Project for the Construction of a High-level University of Guangzhou Medical University, China
- 31010406110 Postdoctoral Research Startup Funding, China
- 31010406110 Postdoctoral Research Startup Funding, China
- 31010406110 Postdoctoral Research Startup Funding, China
- 31010406110 Postdoctoral Research Startup Funding, China
- 31010406110 Postdoctoral Research Startup Funding, China
- 31010406110 Postdoctoral Research Startup Funding, China
Collapse
Affiliation(s)
- Bo Zhu
- Department of Urology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
- Guangdong Key Laboratory of Urology, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
| | - Yuxi Nie
- Department of Urology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
- Guangdong Key Laboratory of Urology, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
| | - Sijie Zheng
- The Second Clinical College, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
| | - Shutong Lin
- The Second Clinical College, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
| | - Zhen Li
- Department of Urology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
- Guangdong Key Laboratory of Urology, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
- The Second Clinical College, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China
| | - Wenqi Wu
- Department of Urology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China.
- Guangdong Key Laboratory of Urology, Guangzhou Medical University, Guangzhou, 510260, Guangdong, China.
| |
Collapse
|
|
8
|
Kang Z, Xiao E, Li Z, Wang L. Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions. Acad Radiol 2024; 31:2412-2423. [PMID: 38302387 DOI: 10.1016/j.acra.2023.12.042] [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: 11/13/2023] [Revised: 12/25/2023] [Accepted: 12/30/2023] [Indexed: 02/03/2024]
Abstract
RATIONALE AND OBJECTIVES To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. MATERIALS AND METHODS From January 2015 to December 2021, lesions diagnosed with PI-RADS 3 by multi-parametric MRI or bi-parametric MRI were retrospectively included. They were classified as benign prostate lesions, non-csPCa, and csPCa according to the pathological results. T2-weighted images of the lesions were divided into a training set and a test set according to 8:2. ResNet-18 was used for model training. All statistical analyses were performed using Python open-source libraries. The receiver operating characteristic curve (ROC) was used to evaluate the predictive effectiveness of the model. T-SNE was used for image semantic feature visualization. The class activation mapping was used to visualize the area focused by the model. RESULTS A total of 428 benign prostate lesion images, 158 non-csPCa images and 273 csPCa images were included. The precision in predicting benign prostate disease, non-csPCa and csPCa were 0.882, 0.681 and 0.851, and the area under the ROC were 0.875, 0.89 and 0.929, respectively. Semantic feature analysis showed strong classification separability between csPCa and benign prostate lesions. The class activation map showed that the deep learning model can focus on the area of the prostate or the location of PI-RADS 3 lesions. CONCLUSION Deep learning model with T2-weighted images based on ResNet-18 can realize accurate classification of PI-RADS 3 lesions.
Collapse
Affiliation(s)
- Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Enhua Xiao
- Department of Radiology, the Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
|
9
|
Hindocha S, Hunter B, Linton-Reid K, George Charlton T, Chen M, Logan A, Ahmed M, Locke I, Sharma B, Doran S, Orton M, Bunce C, Power D, Ahmad S, Chan K, Ng P, Toshner R, Yasar B, Conibear J, Murphy R, Newsom-Davis T, Goodley P, Evison M, Yousaf N, Bitar G, McDonald F, Blackledge M, Aboagye E, Lee R. Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis. Radiother Oncol 2024; 195:110266. [PMID: 38582181 DOI: 10.1016/j.radonc.2024.110266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.
Collapse
Affiliation(s)
- Sumeet Hindocha
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK.
| | - Benjamin Hunter
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Kristofer Linton-Reid
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Thomas George Charlton
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Andrew Logan
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Imogen Locke
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Bhupinder Sharma
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Simon Doran
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Matthew Orton
- Artificial Intelligence Imaging Hub, Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Catey Bunce
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Danielle Power
- Department of Clinical Oncology, Imperial College Healthcare NHS Trust, Fulham Palace Road, London W6 8RF, UK
| | - Shahreen Ahmad
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Karen Chan
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Peng Ng
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Richard Toshner
- Interstitial lung disease unit, St Bartholomews' Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Binnaz Yasar
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - John Conibear
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Ravindhi Murphy
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Tom Newsom-Davis
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Patrick Goodley
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK; Division of Immunology, Immunity to Infection & Respiratory Medicine, University of Manchester, Manchester, UK
| | - Matthew Evison
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK
| | - Nadia Yousaf
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - George Bitar
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Fiona McDonald
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Matthew Blackledge
- Radiotherapy and Imaging, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Richard Lee
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| |
Collapse
|
|
10
|
Meng H, Wang TD, Zhuo LY, Hao JW, Sui LY, Yang W, Zang LL, Cui JJ, Wang JN, Yin XP. Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia. Front Med (Lausanne) 2024; 11:1409477. [PMID: 38831994 PMCID: PMC11146305 DOI: 10.3389/fmed.2024.1409477] [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: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.
Collapse
Affiliation(s)
- Huan Meng
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Tian-Da Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Li-Yong Zhuo
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jia-Wei Hao
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Lian-yu Sui
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Wei Yang
- Department of Radiology, Baoding First Central Hospital, Baoding, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, Baoding, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Beijing, China
| | - Jia-Ning Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| |
Collapse
|
|
11
|
Chen L, Zeng B, Shen J, Xu J, Cai Z, Su S, Chen J, Cai X, Ying T, Hu B, Wu M, Chen X, Zheng Y. Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study. BMJ Open 2024; 14:e079969. [PMID: 38401893 PMCID: PMC10895244 DOI: 10.1136/bmjopen-2023-079969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/31/2024] [Indexed: 02/26/2024] Open
Abstract
INTRODUCTION Radiographic bone age (BA) assessment is widely used to evaluate children's growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of this study is to develop a new, safer, radiation-free BA assessment method for children by using three-dimensional ultrasound (3D-US) and artificial intelligence (AI), and to test the diagnostic accuracy and reliability of this method. METHODS AND ANALYSIS This is a prospective, observational study. All participants will be recruited through Paediatric Growth and Development Clinic. All participants will receive left hand 3D-US and X-ray examination at the Shanghai Sixth People's Hospital on the same day, all images will be recorded. These image related data will be collected and randomly divided into training set (80% of all) and test set (20% of all). The training set will be used to establish a cascade network of 3D-US skeletal image segmentation and BA prediction model to achieve end-to-end prediction of image to BA. The test set will be used to evaluate the accuracy of AI BA model of 3D-US. We have developed a new ultrasonic scanning device, which can be proposed to automatic 3D-US scanning of hands. AI algorithms, such as convolutional neural network, will be used to identify and segment the skeletal structures in the hand 3D-US images. We will achieve automatic segmentation of hand skeletal 3D-US images, establish BA prediction model of 3D-US, and test the accuracy of the prediction model. ETHICS AND DISSEMINATION The Ethics Committee of Shanghai Sixth People's Hospital approved this study. The approval number is 2022-019. A written informed consent will be obtained from their parent or guardian of each participant. Final results will be published in peer-reviewed journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER ChiCTR2200057236.
Collapse
Affiliation(s)
- Li Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bolun Zeng
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Shen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiangchang Xu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zehang Cai
- Shantou Institute of Ultrasonic Instruments Co., Ltd, Shantou, China
| | - Shudian Su
- Shantou Institute of Ultrasonic Instruments Co., Ltd, Shantou, China
| | - Jie Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Cai
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Ying
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Hu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Wu
- Department of Pediatrics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyi Zheng
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
|
12
|
Li Y, Chen D, Liu S, Lin J, Wang W, Huang J, Tan L, Liang L, Wang Z, Peng K, Li Q, Jian W, Zhang Y, Peng C, Chen H, Zhang X, Zheng J. Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT. Mycoses 2024; 67:e13692. [PMID: 38214431 DOI: 10.1111/myc.13692] [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: 10/04/2023] [Revised: 12/16/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.
Collapse
Affiliation(s)
- Yun Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Deyan Chen
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Shuyi Liu
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Junfeng Lin
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wei Wang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jinhai Huang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lunfang Tan
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lina Liang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhufeng Wang
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kang Peng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiasheng Li
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenhua Jian
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Youwen Zhang
- Department of Neurology, Gaozhou People's Hospital, Gaozhou, China
| | - Chengbao Peng
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Huai Chen
- Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xia Zhang
- Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd, Shenyang, China
| | - Jinping Zheng
- National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
|
13
|
Wang W, Liang H, Zhang Z, Xu C, Wei D, Li W, Qian Y, Zhang L, Liu J, Lei D. Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study. EClinicalMedicine 2024; 67:102385. [PMID: 38261897 PMCID: PMC10796944 DOI: 10.1016/j.eclinm.2023.102385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 01/25/2024] Open
Abstract
Background The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC. Methods In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis. Findings The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73-0.78, P = 0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P = 0.025-0.46), 0.86-0.89 (P = 0.75-0.97), 0.83-0.86 (P = 0.029-0.66), and 0.79-0.82 (P = 0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets. Interpretation The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease. Funding National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.
Collapse
Affiliation(s)
- Wenlun Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Hui Liang
- Department of Otorhinolaryngology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Ji’nan 250014, Shandong, China
| | - Zhouyi Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Chenyang Xu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Dongmin Wei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Wenming Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Ye Qian
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| | - Lihong Zhang
- Department of Otorhinolaryngology Head & Neck Surgery, Peking University People’s Hospital, Beijing 100044, China
| | - Jun Liu
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Dapeng Lei
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China
- NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, Shandong, China
| |
Collapse
|
|
14
|
Kawata N, Iwao Y, Matsuura Y, Suzuki M, Ema R, Sekiguchi Y, Sato H, Nishiyama A, Nagayoshi M, Takiguchi Y, Suzuki T, Haneishi H. Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19. Jpn J Radiol 2023; 41:1359-1372. [PMID: 37440160 PMCID: PMC10687147 DOI: 10.1007/s11604-023-01466-3] [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: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. MATERIALS AND METHODS We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. RESULTS The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. CONCLUSIONS The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.
Collapse
Affiliation(s)
- Naoko Kawata
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan.
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan.
- Medical Mycology Research Center (MMRC), Chiba University, Chiba, 260-8673, Japan.
| | - Yuma Iwao
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
- Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan
| | - Yukiko Matsuura
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan
| | - Masaki Suzuki
- Department of Respirology, Kashiwa Kousei General Hospital, 617 Shikoda, Kashiwa-shi, Chiba, 277-8551, Japan
| | - Ryogo Ema
- Department of Respirology, Eastern Chiba Medical Center, 3-6-2, Okayamadai, Togane-shi, Chiba, 283-8686, Japan
| | - Yuki Sekiguchi
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Hirotaka Sato
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan
- Department of Radiology, Soka Municipal Hospital, 2-21-1, Souka, Souka-shi, Saitama, 340-8560, Japan
| | - Akira Nishiyama
- Department of Radiology, Chiba University Hospital, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan
| | - Masaru Nagayoshi
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan
| | - Yasuo Takiguchi
- Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan
| | - Takuji Suzuki
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
| |
Collapse
|
|
15
|
Wu P, Jiang Y, Xing H, Song W, Cui X, Wu XL, Xu G. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Phys Med Biol 2023; 68:175023. [PMID: 37524093 DOI: 10.1088/1361-6560/acec2d] [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: 04/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.
Collapse
Affiliation(s)
- Peiyan Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Yan Jiang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Hanshuo Xing
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Wenbo Song
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xing Long Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| |
Collapse
|
|
16
|
Wang L, Zhou H, Xu N, Liu Y, Jiang X, Li S, Feng C, Xu H, Deng K, Song J. A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience 2023; 26:107005. [PMID: 37534183 PMCID: PMC10391673 DOI: 10.1016/j.isci.2023.107005] [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: 10/14/2022] [Revised: 04/27/2023] [Accepted: 05/26/2023] [Indexed: 08/04/2023] Open
Abstract
Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.
Collapse
Affiliation(s)
- Lu Wang
- Department of Library, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - He Zhou
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Nan Xu
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Yuchan Liu
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Shenyang, Liaoning 110169, China
| | - Hainan Xu
- Department of Obstetrics and Gynecology, Pelvic Floor Disease Diagnosis and Treatment Center, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| |
Collapse
|
|
17
|
Troiano G, Fanelli F, Rapani A, Zotti M, Lombardi T, Zhurakivska K, Stacchi C. Can radiomic features extracted from intra-oral radiographs predict physiological bone remodelling around dental implants? A hypothesis-generating study. J Clin Periodontol 2023; 50:932-941. [PMID: 36843362 DOI: 10.1111/jcpe.13797] [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: 11/14/2022] [Revised: 02/02/2023] [Accepted: 02/19/2023] [Indexed: 02/28/2023]
Abstract
AIM The rate of physiological bone remodelling (PBR) occurring after implant placement has been associated with the later onset of progressive bone loss and peri-implantitis, leading to medium- and long-term implant therapy failure. It is still questionable, however, whether PBR is associated with specific bone characteristics. The aim of this study was to assess whether radiomic analysis could reveal not readily appreciable bone features useful for the prediction of PBR. MATERIALS AND METHODS Radiomic features were extracted from the radiographs taken at implant placement (T0) using LifeX software. Because of the multi-centre design of the source study, ComBat harmonization was applied to the cohort. Different machine-learning models were trained on selected radiomic features to develop and internally validate algorithms capable of predicting high PBR. In addition, results of the algorithm were included in a multivariate analysis with other clinical variables (tissue thickness and depth of implant position) to test their independent correlation with PBR. RESULTS Specific radiomic features extracted at T0 are associated with higher PBR around tissue-level implants after 3 months of unsubmerged healing (T1). In addition, taking advantage of machine-learning methods, a naive Bayes model was trained using radiomic features selected by fast correlation-based filter (FCBF), which showed the best performance in the prediction of PBR (AUC = 0.751, sensitivity = 66.0%, specificity = 68.4%, positive predictive value = 73.3%, negative predictive value = 60.5%). In addition, results of the whole model were included in a multivariate analysis with tissue thickness and depth of implant position, which were still found to be independently associated with PBR (p-value < .01). CONCLUSION The combination of radiomics and machine-learning methods seems to be a promising approach for the early prediction of PBR. Such an innovative approach could be also used for the study of not readily disclosed bone characteristics, thus helping to explain not fully understood clinical phenomena. Although promising, the performance of the radiomic model should be improved in terms of specificity and sensitivity by further studies in this field.
Collapse
Affiliation(s)
- Giuseppe Troiano
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Francesco Fanelli
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Antonio Rapani
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Matteo Zotti
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Teresa Lombardi
- Department of Health Sciences, University "Magna Graecia", Catanzaro, Italy
| | - Khrystyna Zhurakivska
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Claudio Stacchi
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| |
Collapse
|
|
18
|
Yu X, Kang B, Nie P, Deng Y, Liu Z, Mao N, An Y, Xu J, Huang C, Huang Y, Zhang Y, Hou Y, Zhang L, Sun Z, Zhu B, Shi R, Zhang S, Sun C, Wang X. Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study. Chin Med J (Engl) 2023; 136:1188-1197. [PMID: 37083119 PMCID: PMC10278712 DOI: 10.1097/cm9.0000000000002671] [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: 07/26/2022] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Collapse
Affiliation(s)
- Xinxin Yu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000, China
| | - Yan Deng
- Department of Radiology, Qilu Hospital, Shandong University, Jinan, Shandong 250012, China
| | - Zixin Liu
- Department of Medicine, Graduate School, Kyung Hee University, Seoul 446701, Republic of Korea
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong 164000, China
| | - Yahui An
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Yong Huang
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Yonggao Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Zhanguo Sun
- Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong 272029, China
| | - Baosen Zhu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Rongchao Shi
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Shuai Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| |
Collapse
|
|
19
|
Gifani P, Vafaeezadeh M, Ghorbani M, Mehri-Kakavand G, Pursamimi M, Shalbaf A, Davanloo AA. Automatic Diagnosis of Stage of COVID-19 Patients using an Ensemble of Transfer Learning with Convolutional Neural Networks Based on Computed Tomography Images. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:101-109. [PMID: 37448543 PMCID: PMC10336907 DOI: 10.4103/jmss.jmss_158_21] [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: 09/20/2021] [Revised: 01/13/2022] [Accepted: 04/19/2022] [Indexed: 07/15/2023]
Abstract
Background Diagnosis of the stage of COVID-19 patients using the chest computed tomography (CT) can help the physician in making decisions on the length of time required for hospitalization and adequate selection of patient care. This diagnosis requires very expert radiologists who are not available everywhere and is also tedious and subjective. The aim of this study is to propose an advanced machine learning system to diagnose the stages of COVID-19 patients including normal, early, progressive, peak, and absorption stages based on lung CT images, using an automatic deep transfer learning ensemble. Methods Different strategies of deep transfer learning were used which were based on pretrained convolutional neural networks (CNNs). Pretrained CNNs were fine-tuned on the chest CT images, and then, the extracted features were classified by a softmax layer. Finally, we built an ensemble method based on majority voting of the best deep transfer learning outputs to further improve the recognition performance. Results The experimental results from 689 cases indicate that the ensemble of three deep transfer learning outputs based on EfficientNetB4, InceptionResV3, and NasNetlarge has the highest results in diagnosing the stage of COVID-19 with an accuracy of 91.66%. Conclusion The proposed method can be used for the classification of the stage of COVID-19 disease with good accuracy to help the physician in making decisions on patient care.
Collapse
Affiliation(s)
- Parisa Gifani
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Majid Vafaeezadeh
- Department of Biomedical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohamad Pursamimi
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | |
Collapse
|
|
20
|
Karpov OE, Pitsik EN, Kurkin SA, Maksimenko VA, Gusev AV, Shusharina NN, Hramov AE. Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5335. [PMID: 37047950 PMCID: PMC10094658 DOI: 10.3390/ijerph20075335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.
Collapse
Affiliation(s)
- Oleg E. Karpov
- National Medical and Surgical Center Named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | - Elena N. Pitsik
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Semen A. Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Vladimir A. Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander V. Gusev
- K-Skai LLC, 185031 Petrozavodsk, Russia
- Federal Research Institute for Health Organization and Informatics, 127254 Moscow, Russia
| | - Natali N. Shusharina
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| | - Alexander E. Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia; (E.N.P.); (S.A.K.); (V.A.M.); (N.N.S.)
| |
Collapse
|
|
21
|
Demircioğlu A. Are deep models in radiomics performing better than generic models? A systematic review. Eur Radiol Exp 2023; 7:11. [PMID: 36918479 PMCID: PMC10014394 DOI: 10.1186/s41747-023-00325-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/13/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Application of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, and statistical features defined by formulas. Recently, deep learning methods were applied. It is unclear whether deep models (DMs) can outperform generic models (GMs). METHODS We identified publications on PubMed and Embase to determine differences between DMs and GMs in terms of receiver operating area under the curve (AUC). RESULTS Of 1,229 records (between 2017 and 2021), 69 studies were included, 61 (88%) on tumours, 68 (99%) retrospective, and 39 (56%) single centre; 30 (43%) used an internal validation cohort; and 18 (26%) applied cross-validation. Studies with independent internal cohort had a median training sample of 196 (range 41-1,455); those with cross-validation had only 133 (43-1,426). Median size of validation cohorts was 73 (18-535) for internal and 94 (18-388) for external. Considering the internal validation, in 74% (49/66), the DMs performed better than the GMs, vice versa in 20% (13/66); no difference in 6% (4/66); and median difference in AUC 0.045. On the external validation, DMs were better in 65% (13/20), GMs in 20% (4/20) cases; no difference in 3 (15%); and median difference in AUC 0.025. On internal validation, fused models outperformed GMs and DMs in 72% (20/28), while they were worse in 14% (4/28) and equal in 14% (4/28); median gain in AUC was + 0.02. On external validation, fused model performed better in 63% (5/8), worse in 25% (2/8), and equal in 13% (1/8); median gain in AUC was + 0.025. CONCLUSIONS Overall, DMs outperformed GMs but in 26% of the studies, DMs did not outperform GMs.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| |
Collapse
|
|
22
|
Li D, Li X, Li S, Qi M, Sun X, Hu G. Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning. Heliyon 2023; 9:e14374. [PMID: 36942252 PMCID: PMC10023952 DOI: 10.1016/j.heliyon.2023.e14374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/11/2023] Open
Abstract
Background Long-term differential expression of disease-associated genes is a crucial driver of pathological changes in mucinous gastric carcinoma. Therefore, there should be a correlation between depth features extracted from pathology-based full-scan images using deep learning and disease-associated gene expression. This study tried to provides preliminary evidence that long-term differentially expressed (disease-associated) genes lead to subtle changes in disease pathology by exploring their correlation, and offer a new ideas for precise analysis of pathomics and combined analysis of pathomics and genomics. Methods Full pathological scans, gene sequencing data, and clinical data of patients with mucinous gastric carcinoma were downloaded from TCGA data. The VGG-16 network architecture was used to construct a binary classification model to explore the potential of VGG-16 applications and extract the deep features of the pathology-based full-scan map. Differential gene expression analysis was performed and a protein-protein interaction network was constructed to screen disease-related core genes. Differential, Lasso regression, and extensive correlation analyses were used to screen for valuable deep features. Finally, a correlation analysis was used to determine whether there was a correlation between valuable deep features and disease-related core genes. Result The accuracy of the binary classification model was 0.775 ± 0.129. A total of 24 disease-related core genes were screened, including ASPM, AURKA, AURKB, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDCA8, CDK1, CENPF, DLGAP5, KIF11, KIF20A, KIF2C, KIF4A, MELK, PBK, RRM2, TOP2A, TPX2, TTK, UBE2C, and ZWINT. In addition, differential, Lasso regression, and extensive correlation analyses were used to screen eight valuable deep features, including features 51, 106, 109, 118, 257, 282, 326, and 487. Finally, the results of the correlation analysis suggested that valuable deep features were either positively or negatively correlated with core gene expression. Conclusion The preliminary results of this study support our hypotheses. Deep learning may be an important bridge for the joint analysis of pathomics and genomics and provides preliminary evidence for long-term abnormal expression of genes leading to subtle changes in pathology.
Collapse
Affiliation(s)
- Ding Li
- Department of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaoyuan Li
- Department of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shifang Li
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Mengmeng Qi
- Department of Endocrinology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaowei Sun
- Department of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guojie Hu
- Department of Traditional Chinese Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
|
23
|
Pan L, He T, Huang Z, Chen S, Zhang J, Zheng S, Chen X. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image. Abdom Radiol (NY) 2023; 48:1246-1259. [PMID: 36859730 DOI: 10.1007/s00261-023-03838-9] [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: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/03/2023]
Abstract
OBJECTIVES Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. METHODS We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. CONCLUSION The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.
Collapse
Affiliation(s)
- Lin Pan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Tian He
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Zihan Huang
- School of Future Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Shuai Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Junrong Zhang
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Shaohua Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
| | - Xianqiang Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| |
Collapse
|
|
24
|
Yu X, Zhang S, Xu J, Huang Y, Luo H, Huang C, Nie P, Deng Y, Mao N, Zhang R, Gao L, Li S, Kang B, Wang X. Nomogram Using CT Radiomics Features for Differentiation of Pneumonia-Type Invasive Mucinous Adenocarcinoma and Pneumonia: Multicenter Development and External Validation Study. AJR Am J Roentgenol 2023; 220:224-234. [PMID: 36102726 DOI: 10.2214/ajr.22.28139] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND. Pneumonia-type invasive mucinous adenocarcinoma (IMA) and pneumonia show overlapping chest CT features as well as overlapping clinical characteristics. OBJECTIVE. The purpose of our study was to develop and validate a nomogram combining clinical and CT-based radiomics features to differentiate pneumonia-type IMA and pneumonia. METHODS. This retrospective study included 314 patients (172 men, 142 women; mean age, 60.3 ± 14.5 [SD] years) from six hospitals who underwent noncontrast chest CT showing consolidation and were diagnosed with pneumonia-type IMA (n = 106) or pneumonia (n = 208). Patients from three hospitals formed a training set (n = 195) and a validation set (n = 50), and patients from the other three hospitals formed the external test set (n = 69). A model for predicting pneumonia-type IMA was built using clinical characteristics that were significant independent predictors of this diagnosis. Radiomics features were extracted from CT images by placing ROIs on areas of consolidation, and a radiomics signature of pneumonia-type IMA was constructed. A nomogram for predicting pneumonia-type IMA was constructed that combined features in the clinical model and the radiomics signature. Two cardiothoracic radiologists independently reviewed CT images in the external test set to diagnose pneumonia-type IMA. Diagnostic performance was compared among models and radiologists. Decision curve analysis (DCA) was performed. RESULTS. The clinical model included fever and family history of lung cancer. The radiomics signature included 15 radiomics features. DCA showed higher overall net benefit from the nomogram than from the clinical model. In the external test set, AUC was higher for the nomogram (0.85) than for the clinical model (0.71, p = .01), radiologist 1 (0.70, p = .04), and radiologist 2 (0.67, p = .01). In the external test set, the nomogram had sensitivity of 46.9%, specificity of 94.6%, and accuracy of 72.5%. CONCLUSION. The nomogram combining clinical variables and CT-based radiomics features outperformed the clinical model and two cardiothoracic radiologists in differentiating pneumonia-type IMA from pneumonia. CLINICAL IMPACT. The findings support potential clinical use of the nomogram for diagnosing pneumonia-type IMA in patients with consolidation on chest CT.
Collapse
Affiliation(s)
- Xinxin Yu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yong Huang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hao Luo
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Pei Nie
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yan Deng
- Department of Radiology, Qilu Hospital, Shandong University, Jinan, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ran Zhang
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Lin Gao
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Sha Li
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, No. 324, Jingwu Rd, Jinan, 250021, China
| |
Collapse
|
|
25
|
Jeong YJ, Wi YM, Park H, Lee JE, Kim SH, Lee KS. Current and Emerging Knowledge in COVID-19. Radiology 2023; 306:e222462. [PMID: 36625747 PMCID: PMC9846833 DOI: 10.1148/radiol.222462] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/11/2023]
Abstract
COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperinflammatory phases. Clinical features, imaging features, and management are different among the three phases. In the early stage, peripheral ground-glass opacities are predominant CT findings, and therapy directly targeting SARS-CoV-2 is effective. In the later stage, organizing pneumonia or diffuse alveolar damage pattern are predominant CT findings and anti-inflammatory therapies are more beneficial. The risk of severe disease or hospitalization is lower in breakthrough or Omicron variant infection compared with nonimmunized or Delta variant infections. The protection rates of the fourth dose of mRNA vaccination were 34% and 67% against overall infection and hospitalizations for severe illness, respectively. After acute COVID-19 pneumonia, most residual CT abnormalities gradually decreased in extent, but they may remain as linear or multifocal reticular or cystic lesions. Advanced insights into the pathophysiologic and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post-COVID-19 condition, or long COVID, also presents radiology with new challenges.
Collapse
Affiliation(s)
- Yeon Joo Jeong
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Yu Mi Wi
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Hyunjin Park
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Jong Eun Lee
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Si-Ho Kim
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Kyung Soo Lee
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| |
Collapse
|
|
26
|
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.
Collapse
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
| |
Collapse
|
|
27
|
Chung YW, Choi IY. Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder. Sci Rep 2023; 13:1765. [PMID: 36720904 PMCID: PMC9889739 DOI: 10.1038/s41598-023-28082-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 01/12/2023] [Indexed: 02/02/2023] Open
Abstract
We sought to establish an unsupervised algorithm with a three-dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.
Collapse
Affiliation(s)
- Yeon Woong Chung
- Department of Ophthalmology and Visual Science, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.,Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Banpo Dae-Ro 222, Seoul, 06591, Republic of Korea
| | - In Young Choi
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Banpo Dae-Ro 222, Seoul, 06591, Republic of Korea.
| |
Collapse
|
|
28
|
Zorzi G, Berta L, Rizzetto F, De Mattia C, Felisi MMJ, Carrazza S, Nerini Molteni S, Vismara C, Scaglione F, Vanzulli A, Torresin A, Colombo PE. Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches. Eur Radiol Exp 2023; 7:3. [PMID: 36690869 PMCID: PMC9870776 DOI: 10.1186/s41747-022-00317-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.
Collapse
Affiliation(s)
- Giulia Zorzi
- Postgraduate School of Medical Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Luca Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Francesco Rizzetto
- Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Marco Maria Jacopo Felisi
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Stefano Carrazza
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Silvia Nerini Molteni
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Chiara Vismara
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Francesco Scaglione
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Paola Enrica Colombo
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| |
Collapse
|
|
29
|
Chang CC, Tang EK, Wei YF, Lin CY, Wu FZ, Wu MT, Liu YS, Yen YT, Ma MC, Tseng YL. Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan. Front Oncol 2023; 13:1105100. [PMID: 37143945 PMCID: PMC10151670 DOI: 10.3389/fonc.2023.1105100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs). Methods A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models. Result In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275). Conclusion Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.
Collapse
Affiliation(s)
- Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - En-Kuei Tang
- Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yu-Feng Wei
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Chest Medicine, Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan
| | - Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Education, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Ming-Ting Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Mi-Chia Ma
- Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan
- *Correspondence: Yi-Ting Yen, ; Mi-Chia Ma,
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| |
Collapse
|
|
30
|
Chen P, Yang Z, Zhang H, Huang G, Li Q, Ning P, Yu H. Personalized intrahepatic cholangiocarcinoma prognosis prediction using radiomics: Application and development trend. Front Oncol 2023; 13:1133867. [PMID: 37035147 PMCID: PMC10076873 DOI: 10.3389/fonc.2023.1133867] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.
Collapse
Affiliation(s)
- Pengyu Chen
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Zhenwei Yang
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Haofeng Zhang
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Guan Huang
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingshan Li
- Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Peigang Ning
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Haibo Yu
- Department of Hepatobiliary Surgery, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, China
- Department of Hepatobiliary Surgery, People’s Hospital of Zhengzhou University, Zhengzhou, China
- Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, Zhengzhou, China
- *Correspondence: Haibo Yu,
| |
Collapse
|
|
31
|
Aminu M, Yadav D, Hong L, Young E, Edelkamp P, Saad M, Salehjahromi M, Chen P, Sujit SJ, Chen MM, Sabloff B, Gladish G, de Groot PM, Godoy MCB, Cascone T, Vokes NI, Zhang J, Brock KK, Daver N, Woodman SE, Tawbi HA, Sheshadri A, Lee JJ, Jaffray D, Wu CC, Chung C, Wu J. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19. Cancers (Basel) 2022; 15:275. [PMID: 36612278 PMCID: PMC9818576 DOI: 10.3390/cancers15010275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.
Collapse
Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Divya Yadav
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Elliana Young
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Paul Edelkamp
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Maliazurina Saad
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Melissa M. Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Bradley Sabloff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Patricia M. de Groot
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Kristy K. Brock
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Naval Daver
- Department of Leukemia, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Scott E. Woodman
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Hussein A. Tawbi
- Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | | | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Caroline Chung
- Office of the Chief Data Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| |
Collapse
|
|
32
|
Li B, Jiang L, Lin D, Dong J. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov. Diagnostics (Basel) 2022; 12:3046. [PMID: 36553052 PMCID: PMC9777443 DOI: 10.3390/diagnostics12123046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/06/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Clinical trials are the most effective tools to evaluate the advantages of various diagnostic and treatment modalities. AI used in medical issues, including screening, diagnosis, and treatment decisions, improves health outcomes and patient experiences. This study's objective was to investigate the traits of registered trials on artificial intelligence for lung disease. Clinical studies on AI for lung disease that were present in the ClinicalTrials.gov database were searched, and fifty-three registered trials were included. Forty-six (72.1%) were observational trials, compared to seven (27.9%) that were interventional trials. Only eight trials (15.4%) were completed. Thirty (56.6%) trials were accepting applicants. Clinical studies often included a large number of cases; for example, 24 (32.0%) trials included samples of 100-1000 cases, while 14 (17.5%) trials included samples of 1000-2000 cases. Of the interventional trials, twenty (15.7%) were retrospective studies and twenty (65.7%) were prospective studies.
Collapse
Affiliation(s)
- Bingjie Li
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Lisha Jiang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Dan Lin
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Jingsi Dong
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| |
Collapse
|
|
33
|
Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
Collapse
Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
| |
Collapse
|
|
34
|
Lin Q, Wu HJ, Song QS, Tang YK. CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy. Front Oncol 2022; 12:937277. [PMID: 36267975 PMCID: PMC9577189 DOI: 10.3389/fonc.2022.937277] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives In radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT). Materials and methods We reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalization. Spearman correlation coefficient, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) were used to screen features. The pretreatment traditional radiomics combined with clinical characteristics (before_rad_cil) model and pretreatment deep learning characteristics (before_dl) model were constructed according to the data collected before treatment. The data collected after NAT created the after_rad_cil model and after_dl model. The entire model was jointly constructed by all clinical features, conventional radiomics features, and deep learning features before and after neoadjuvant treatment. Finally, according to the data obtained before and after treatment, the before_nomogram and after_nomogram were constructed. Results In the before_rad_cil model, four traditional radiomics features ("original_shape_flatness," "wavelet hhl_firer_skewness," "wavelet hlh_firer_skewness," and "wavelet lll_glcm_correlation") and two clinical features ("gender" and "N stage") were screened out to predict a GPR. The average prediction accuracy (ACC) after modeling with k-nearest neighbor (KNN) was 0.707. In the after_rad_cil model, nine features predictive of GPR were obtained after feature screening, among which seven were traditional radiomics features: "exponential_firer_skewness," "exponential_glrlm_runentropy," "log- sigma-5-0-mm-3d_firer_kurtosis," "logarithm_skewness," "original_shape_elongation," "original_shape_brilliance," and "wavelet llh_glcm_clustershade"; two were clinical features: "after_CRP" and "after lymphocyte percentage." The ACC after modeling with support vector machine (SVM) was 0.682. The before_dl model and after_dl model were modeled by SVM, and the ACC was 0.629 and 0.603, respectively. After feature screening, the entire model was constructed by multilayer perceptron (MLP), and the ACC of the GPR was the highest, 0.805. The calibration curve showed that the predictions of the GPR by the before_nomogram and after_nomogram were in consensus with the actual GPR. Conclusion CT-based radiomics has a good predictive ability for a GPR in NSCLC patients receiving immunotherapy-based NAT. Among the radiomics features combined with the clinicopathological information model, deep learning feature model, and the entire model, the entire model had the highest prediction accuracy.
Collapse
Affiliation(s)
| | - Hai Jun Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | | | | |
Collapse
|
|
35
|
Kong J, He Y, Zhu X, Shao P, Xu Y, Chen Y, Coatrieux JL, Yang G. BKC-Net: Bi-Knowledge Contrastive Learning for renal tumor diagnosis on 3D CT images. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
|
36
|
Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
Collapse
Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
|
37
|
Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
Collapse
|
|
38
|
Mehrpouyan M, Zamanian H, Mehri-Kakavand G, Pursamimi M, Shalbaf A, Ghorbani M, Abbaskhani Davanloo A. Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach. Phys Eng Sci Med 2022; 45:747-755. [PMID: 35796865 PMCID: PMC9261171 DOI: 10.1007/s13246-022-01140-4] [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: 06/15/2021] [Accepted: 05/16/2022] [Indexed: 11/22/2022]
Abstract
The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.
Collapse
Affiliation(s)
- Mohammad Mehrpouyan
- Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran.,Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Hamed Zamanian
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohamad Pursamimi
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Ahmad Shalbaf
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
| | - Mahdi Ghorbani
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
| | | |
Collapse
|
|
39
|
Duan C, Ma R, Zeng X, Chen B, Hou D, Liu R, Li X, Liu L, Li T, Huang H. SARS-CoV-2 Achieves Immune Escape by Destroying Mitochondrial Quality: Comprehensive Analysis of the Cellular Landscapes of Lung and Blood Specimens From Patients With COVID-19. Front Immunol 2022; 13:946731. [PMID: 35844544 PMCID: PMC9283956 DOI: 10.3389/fimmu.2022.946731] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 12/15/2022] Open
Abstract
Mitochondria get caught in the crossfire of coronavirus disease 2019 (COVID-19) and antiviral immunity. The mitochondria-mediated antiviral immunity represents the host’s first line of defense against viral infection, and the mitochondria are important targets of COVID-19. However, the specific manifestations of mitochondrial damage in patients with COVID-19 have not been systematically clarified. This study comprehensively analyzed one single-cell RNA-sequencing dataset of lung tissue and two bulk RNA-sequencing datasets of blood from COVID-19 patients. We found significant changes in mitochondrion-related gene expression, mitochondrial functions, and related metabolic pathways in patients with COVID-19. SARS-CoV-2 first infected the host alveolar epithelial cells, which may have induced excessive mitochondrial fission, inhibited mitochondrial degradation, and destroyed the mitochondrial calcium uniporter (MCU). The type II alveolar epithelial cell count decreased and the transformation from type II to type I alveolar epithelial cells was blocked, which exacerbated viral immune escape and replication in COVID-19 patients. Subsequently, alveolar macrophages phagocytized the infected alveolar epithelial cells, which decreased mitochondrial respiratory capacity and activated the ROS–HIF1A pathway in macrophages, thereby aggravating the pro-inflammatory reaction in the lungs. Infected macrophages released large amounts of interferon into the blood, activating mitochondrial IFI27 expression and destroying energy metabolism in immune cells. The plasma differentiation of B cells and lung-blood interaction of regulatory T cells (Tregs) was exacerbated, resulting in a cytokine storm and excessive inflammation. Thus, our findings systematically explain immune escape and excessive inflammation seen during COVID-19 from the perspective of mitochondrial quality imbalance.
Collapse
Affiliation(s)
- Chenyang Duan
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Chenyang Duan, ; He Huang,
| | - Ruiyan Ma
- Department of Cardiovascular Surgery, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Xue Zeng
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bing Chen
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongyao Hou
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ruixue Liu
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuehan Li
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liangming Liu
- Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, China
| | - Tao Li
- Department of Shock and Transfusion, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Army Medical University, Chongqing, China
| | - He Huang
- Department of Anesthesiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Chenyang Duan, ; He Huang,
| |
Collapse
|
|
40
|
Shiiba T, Takano K, Takaki A, Suwazono S. Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease. EJNMMI Res 2022; 12:39. [PMID: 35759054 PMCID: PMC9237203 DOI: 10.1186/s13550-022-00910-1] [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: 02/03/2022] [Accepted: 06/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson's disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance. RESULTS We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson's Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SURputamen was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SURputamen in each test dataset. When combined with SURputamen and radiomics signature, all classification models showed slightly higher AUCs than that of SURputamen alone. CONCLUSION We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.
Collapse
Affiliation(s)
- Takuro Shiiba
- Department of Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
| | - Kazuki Takano
- Department of Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Akihiro Takaki
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misakimachi, Omuta-shi, Fukuoka, 836-8505, Japan
| | - Shugo Suwazono
- Department of Neurology and Center for Clinical Neuroscience, National Hospital Organization Okinawa National Hospital, 3-20-14 Ganeko, Ginowan, 901-2214, Okinawa, Japan
| |
Collapse
|
|
41
|
Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts. Diagnostics (Basel) 2022; 12:diagnostics12061464. [PMID: 35741274 PMCID: PMC9221552 DOI: 10.3390/diagnostics12061464] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
This study was designed to develop machine-learning models to predict COVID-19 mortality and identify its key features based on clinical characteristics and laboratory tests. For this, deep-learning (DL) and machine-learning (ML) models were developed using receiver operating characteristic (ROC) area under the curve (AUC) and F1 score optimization of 87 parameters. Of the two, the DL model exhibited better performance (AUC 0.8721, accuracy 0.84, and F1 score 0.76). However, we also blended DL with ML, and the ensemble model performed the best (AUC 0.8811, accuracy 0.85, and F1 score 0.77). The DL model is generally unable to extract feature importance; however, we succeeded by using the Shapley Additive exPlanations method for each model. This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.
Collapse
|
|
42
|
Zhang W, Peng J, Zhao S, Wu W, Yang J, Ye J, Xu S. Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes. J Cancer Res Clin Oncol 2022; 148:2773-2780. [PMID: 35562596 DOI: 10.1007/s00432-022-04047-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 04/27/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes. METHODS The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2. The radiomics features were extracted using one-by-one convolution and neural network activation, filtered with the least absolute shrinkage and selection operator (LASSO) model, and used to construct a discrimination model with PyTorch. Then, the performance of the model was compared with the radiologists' diagnostic performance. The neural network model was evaluated using the area under the receiver-operator characteristic curve (AUC), and the accuracy, sensitivity, and specificity were analysed. RESULTS A total of 102 features, comprising five traditional radiomic features and 97 deep learning features, were selected with LASSO and used to construct a discrimination model, which achieved a total accuracy of 87.50%. The AUC value, specificity, and sensitivity were, respectively, 0.92, 92.30%, and 90.00% for metastatic lymph nodes, 0.87, 95.45%, and 83.33% for benign lymphadenopathy, and 0.88, 90.47%, and 85.71% for lymphoma. The accuracies of the radiologists' diagnoses were 62.68% and 62.68%. The diagnostic performance of the model was significantly different from that of the radiologists (p < 0.05). CONCLUSION CT-based deep learning combined with the traditional radiomics methods has a high diagnostic value for the classification of cervical enlarged lymph nodes.
Collapse
Affiliation(s)
- Wentao Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jian Peng
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Shan Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wenli Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Junjun Yang
- Key Laboratory of Optoelectronic Technology, The Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Junyong Ye
- Key Laboratory of Optoelectronic Technology, The Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Shengsheng Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| |
Collapse
|
|
43
|
Luo Y, Xue Y, Song H, Tang G, Liu W, Bai H, Yuan X, Tong S, Wang F, Cai Y, Sun Z. Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect 2022; 84:648-657. [PMID: 34995637 DOI: 10.1016/j.jinf.2021.12.046] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/18/2021] [Accepted: 12/26/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI. METHODS Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning. RESULTS A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively. CONCLUSIONS We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.
Collapse
Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Huan Bai
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Shutao Tong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong road 13, Wuhan, China.
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| |
Collapse
|
|
44
|
Wu JP, Ding WZ, Wang YL, Liu S, Zhang XQ, Yang Q, Cai WJ, Yu XL, Liu FY, Kong D, Zhong H, Yu J, Liang P. Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation. Int J Hyperthermia 2022; 39:595-604. [PMID: 35435082 DOI: 10.1080/02656736.2022.2062463] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Jia-peng Wu
- School of Medicine, Nankai University, Tianjin, China
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Wen-zhen Ding
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Yu-ling Wang
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Sisi Liu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Xiao-qian Zhang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Qi Yang
- Department of Medical Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China
| | - Wen-jia Cai
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Xiao-ling Yu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Fang-yi Liu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Hui Zhong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi' an, China
| | - Jie Yu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Ping Liang
- School of Medicine, Nankai University, Tianjin, China
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
|
45
|
Nagaraj Y, de Jonge G, Andreychenko A, Presti G, Fink MA, Pavlov N, Quattrocchi CC, Morozov S, Veldhuis R, Oudkerk M, van Ooijen PMA. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol 2022; 32:6384-6396. [PMID: 35362751 PMCID: PMC8973680 DOI: 10.1007/s00330-022-08730-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/13/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022]
Abstract
Objective To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08730-6.
Collapse
Affiliation(s)
- Yeshaswini Nagaraj
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. .,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Gonda de Jonge
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Gabriele Presti
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Matthias A Fink
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg, member of the German Center for Lung Research, Heidelberg, Germany
| | - Nikolay Pavlov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Carlo C Quattrocchi
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Sergey Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Raymond Veldhuis
- Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data management Biometrics (DMB), University of Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.,Institute for DiagNostic Accuracy Research, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| |
Collapse
|
|
46
|
Vaidyanathan A, Guiot J, Zerka F, Belmans F, Van Peufflik I, Deprez L, Danthine D, Canivet G, Lambin P, Walsh S, Occchipinti M, Meunier P, Vos W, Lovinfosse P, Leijenaar RT. An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest CT. ERJ Open Res 2022; 8:00579-2021. [PMID: 35509437 PMCID: PMC8958945 DOI: 10.1183/23120541.00579-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/04/2022] [Indexed: 01/08/2023] Open
Abstract
Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza. A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfectedhttps://bit.ly/3MJrVRi
Collapse
|
|
47
|
|
|
48
|
Wang D, Zhao J, Zhang R, Yan Q, Zhou L, Han X, Qi Y, Yu D. The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years. Front Pediatr 2022; 10:953399. [PMID: 36245722 PMCID: PMC9554402 DOI: 10.3389/fped.2022.953399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/17/2022] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To investigate the value of CT radiomics in the differentiation of mycoplasma pneumoniae pneumonia (MPP) from streptococcus pneumoniae pneumonia (SPP) with similar CT manifestations in children under 5 years. METHODS A total of 102 children with MPP (n = 52) or SPP (n = 50) with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children's Hospital between January 2017 and March 2022 were enrolled in the retrospective study. Radiomic features of the both lesions on plain CT images were extracted including the consolidation part of the pneumonia or both consolidation and surrounding halo area which were respectively delineated at region of interest (ROI) areas on the maximum axial image. The training cohort (n = 71) and the validation cohort (n = 31) were established by stratified random sampling at a ratio of 7:3. By means of variance threshold, the effective radiomics features, SelectKBest and least absolute shrinkage and selection operator (LASSO) regression method were employed for feature selection and combined to calculate the radiomics score (Rad-score). Six classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) were used to construct the models based on radiomic features. The diagnostic performance of these models and the radiomic nomogram was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC), and the decision curve analysis (DCA) was used to evaluate which model achieved the most net benefit. RESULTS RF outperformed other classifiers and was selected as the backbone in the classifier with the consolidation + the surrounding halo was taken as ROI to differentiate MPP from SPP in validation cohort. The AUC value of MPP in validation cohort was 0.822, the sensitivity and specificity were 0.81 and 0.81, respectively. CONCLUSION The RF model has the best classification efficiency in the identification of MPP from SPP in children, and the ROI with both consolidation and surrounding halo is most suitable for the delineation.
Collapse
Affiliation(s)
- Dongdong Wang
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jianshe Zhao
- Department of Radiology, Children's Hospital Affiliated to Shandong University, Jinan, China
| | - Ran Zhang
- Huiying Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Qinghu Yan
- Department of Ultrasound, Shandong Public Health Clinical Center, Jinan, China
| | - Lu Zhou
- Department of Cardiac Surgery ICU, Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoyu Han
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yafei Qi
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| |
Collapse
|
|
49
|
Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021; 49:2462-2481. [PMID: 34939174 PMCID: PMC9206619 DOI: 10.1007/s00259-021-05658-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/12/2021] [Indexed: 10/24/2022]
Abstract
PURPOSE Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
Collapse
|
|
50
|
Castillo T. JM, Arif M, Starmans MPA, Niessen WJ, Bangma CH, Schoots IG, Veenland JF. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers (Basel) 2021; 14:12. [PMID: 35008177 PMCID: PMC8749796 DOI: 10.3390/cancers14010012] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 12/16/2022] Open
Abstract
The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.
Collapse
Affiliation(s)
- Jose M. Castillo T.
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Muhammad Arif
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Martijn P. A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
- Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Chris H. Bangma
- Department of Urology, Erasmus MC, 3015 GD Rotterdam, The Netherlands;
| | - Ivo G. Schoots
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Jifke F. Veenland
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| |
Collapse
|