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Zhang Z, Jia XF, Chen XY, Chen YH, Pan KH. Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging. J Hepatocell Carcinoma 2024; 11:1185-1192. [PMID: 38933179 PMCID: PMC11199320 DOI: 10.2147/jhc.s461420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/01/2024] [Indexed: 06/28/2024] Open
Abstract
Objective The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC). Methods A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. Results There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75-0.88) and 0.73 (0.64-0.80) in the training group and an AUC of 0.74 (0.61-0.85) and 0.62 (0.48-0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78-0.91) and 0.77 (0.64-0.87) in the training and test groups, respectively. Conclusion A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes.
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Affiliation(s)
- Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Xiu-Fen Jia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Xiao-Yu Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Yong-Hua Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Ke-Hua Pan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
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Nong HY, Cen YY, Qin M, Qin WQ, Xie YX, Li L, Liu MR, Ding K. Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study. World J Gastrointest Oncol 2024; 16:1309-1318. [PMID: 38660663 PMCID: PMC11037072 DOI: 10.4251/wjgo.v16.i4.1309] [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: 10/30/2023] [Revised: 12/18/2023] [Accepted: 02/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. METHODS This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). RESULTS Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Department of Radiology, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Mi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Wen-Qi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - You-Xiang Xie
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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Jha AK, Mithun S, Sherkhane UB, Dwivedi P, Puts S, Osong B, Traverso A, Purandare N, Wee L, Rangarajan V, Dekker A. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:569-582. [PMID: 37720353 PMCID: PMC10501896 DOI: 10.37349/etat.2023.00153] [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: 12/22/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Umeshkumar B. Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
| | - Pooj Dwivedi
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
- Department of Nuclear Medicine, Advance Center for Treatment, Research, Education in Cancer, Kharghar, Navi-Mumbai 410210, Maharashtra, India
| | - Senders Puts
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Biche Osong
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
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Liang G, Yu W, Liu S, Zhang M, Xie M, Liu M, Liu W. The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis. Front Oncol 2023; 12:960944. [PMID: 36798691 PMCID: PMC9928182 DOI: 10.3389/fonc.2022.960944] [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: 06/03/2022] [Accepted: 12/23/2022] [Indexed: 02/01/2023] Open
Abstract
Objective The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Method The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks' test was used to assess publication bias. Results A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72-0.85), 0.81 (95%CI: 0.73-0.87), 4.1 (95%CI:2.9-5.9), 0.26 (95%CI: 0.19-0.35), 16 (95%CI: 9-28), and 0.87 (95%CI: 0.84-0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks' test indicates that there was no publication bias (P=0.71). Conclusion Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.
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Affiliation(s)
- Gao Liang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Wei Yu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Shuqin Liu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Mingxing Zhang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Mingguo Xie
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China,*Correspondence: Mingguo Xie,
| | - Min Liu
- Toxicology Department, West China-Frontier PharmaTech Co., Ltd. (WCFP), Chengdu, Sichuan, China
| | - Wenbin Liu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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Bodard S, Liu Y, Guinebert S, Kherabi Y, Asselah T. Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis. Cancers (Basel) 2023; 15:cancers15030743. [PMID: 36765701 PMCID: PMC9913680 DOI: 10.3390/cancers15030743] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. METHODS We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. RESULTS Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84). CONCLUSIONS Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
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Affiliation(s)
- Sylvain Bodard
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- CNRS, INSERM, UMR 7371, Laboratoire d’Imagerie Biomédicale, Sorbonne Université, 75006 Paris, France
- Correspondence: ; Tel.: +33-6-18-81-62-10
| | - Yan Liu
- Faculty of Life Science and Medicine, King’s College London, London WC2R 2LS, UK
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France
| | - Sylvain Guinebert
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Yousra Kherabi
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Tarik Asselah
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- Service d’Hépatologie, INSERM, UMR1149, Hôpital Beaujon, AP-HP.Nord, 92110 Clichy, France
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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Prognostic Value of a CT Radiomics-Based Nomogram for the Overall Survival of Patients with Nonmetastatic BCLC Stage C Hepatocellular Carcinoma after Stereotactic Body Radiotherapy. JOURNAL OF ONCOLOGY 2023; 2023:1554599. [PMID: 36636027 PMCID: PMC9831699 DOI: 10.1155/2023/1554599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/02/2022] [Accepted: 12/15/2022] [Indexed: 01/14/2023]
Abstract
Purpose This study aimed to investigatie the feasibility of pretherapeutic CT radiomics-based nomograms to predict the overall survival (OS) of patients with nondistant metastatic Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT). Methods A retrospective review of 137 patients with nondistant metastatic BCLC-C HCC who underwent SBRT was made. Radiomics features distilled from pretherapeutic CT images were selected by the method of LASSO regression for radiomics signature construction. Then, the clinical model was constructed based on clinical characteristics. A radiomics nomogram was constructed using the radiomics score (Rad-score) and clinical characteristics to predict post-SBRT OS in BCLC-C HCC patients. An analysis of discriminatory ability and calibration was performed to confirm the efficacy of the radiomics nomogram. Results In order to construct the radiomic signature, seven significant features were selected. Patients were divided into low-risk (Rad-score < -0.03) and high-risk (Rad-score ≥ -0.03) groups based on the best Rad-score cutoff value. There were statistically significant differences in OS both in the training set (p < 0.0001) and the validation set (p=0.03) after stratification. The C-indexes of the radiomics nomogram were 0.77 (95% CI: 0.72-0.82) in the training set and 0.71 (95% CI: 0.61-0.81) in the validation set, which outperformed the clinical model and radiomics signature. An AUC of 0.76, 0.79, and 0.84 was reached for 6-, 12-, and 18-month survival predictions, respectively. Conclusions The predictive nomogram that combines radiomic features with clinical characteristics has great prospects for application in the prediction of post-SBRT OS in nondistant metastatic BCLC-C HCC patients.
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Chen YD, Zhang L, Zhou ZP, Lin B, Jiang ZJ, Tang C, Dang YW, Xia YW, Song B, Long LL. Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma. World J Gastroenterol 2022; 28:4399-4416. [PMID: 36159011 PMCID: PMC9453772 DOI: 10.3748/wjg.v28.i31.4399] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/05/2022] [Accepted: 07/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology.
AIM To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC.
METHODS In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability.
RESULTS Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 (P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis.
CONCLUSION AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.
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Affiliation(s)
- Yi-Di Chen
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
| | - Ling Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
| | - Zhi-Peng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Bin Lin
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Zi-Jian Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
| | - Cheng Tang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
| | - Yi-Wu Dang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning 5350021, Guangxi Zhuang Autonomous Region, China
| | - Yu-Wei Xia
- Department of Technology, Huiying Medical Technology (Beijing), Beijing 100192, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
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Li L, Wu C, Huang Y, Chen J, Ye D, Su Z. Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis. Front Oncol 2022; 12:831996. [PMID: 35463303 PMCID: PMC9021380 DOI: 10.3389/fonc.2022.831996] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). To perform a meta-analysis to investigate the diagnostic performance of radiomics for the preoperative evaluation of MVI in HCC and the effect of potential factors. Materials and Methods A systematic literature search was performed in PubMed, Embase, and the Cochrane Library for studies focusing on the preoperative evaluation of MVI in HCC with radiomics methods. Data extraction and quality assessment of the retrieved studies were performed. Statistical analysis included data pooling, heterogeneity testing and forest plot construction. Meta-regression and subgroup analyses were performed to reveal the effect of potential explanatory factors [design, combination of clinical factors, imaging modality, number of participants, and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) applicability risk] on the diagnostic performance. Results Twenty-two studies with 4,129 patients focusing on radiomics for the preoperative prediction of MVI in HCC were included. The pooled sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 84% (95% CI: 81, 87), 83% (95% CI: 78, 87) and 0.90 (95% CI: 0.87, 0.92). Substantial heterogeneity was observed among the studies (I²=94%, 95% CI: 88, 99). Meta-regression showed that all investigative covariates contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Combined clinical factors, MRI, CT and number of participants contributed to the heterogeneity in the specificity analysis (P < 0.05). Subgroup analysis showed that the pooled sensitivity, specificity and AUC estimates were similar among studies with CT or MRI. Conclusion Radiomics is a promising noninvasive method that has high preoperative diagnostic performance for MVI status. Radiomics based on CT and MRI had a comparable predictive performance for MVI in HCC. Prospective, large-scale and multicenter studies with radiomics methods will improve the diagnostic power for MVI in the future. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259363, identifier CRD42021259363.
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Affiliation(s)
- Liujun Li
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Chaoqun Wu
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Yongquan Huang
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Jiaxin Chen
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Dalin Ye
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
| | - Zhongzhen Su
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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12
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Huang H, Ruan SM, Xian MF, Li MD, Cheng MQ, Li W, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Hu HT, Chen LD. Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation. Br J Radiol 2022; 95:20210748. [PMID: 34797687 PMCID: PMC8822579 DOI: 10.1259/bjr.20210748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. METHODS This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. RESULTS The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). CONCLUSION These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. ADVANCES IN KNOWLEDGE CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).
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Affiliation(s)
- Hui Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Si-min Ruan
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Meng-fei Xian
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-de Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Mei-qing Cheng
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yang Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-yan Xie
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | | | | | - Wei Wang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-tong Hu
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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14
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Ren S, Li Q, Liu S, Qi Q, Duan S, Mao B, Li X, Wu Y, Zhang L. Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study. Front Oncol 2021; 11:749137. [PMID: 34804935 PMCID: PMC8604281 DOI: 10.3389/fonc.2021.749137] [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: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE This study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS The clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy. RESULTS The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806-0.989), 0.900, 0.857, 0.868, and 0.874 (0.733-0.961), 0.889, 0.867, and 0.872, respectively. CONCLUSION The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.
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Affiliation(s)
- Shanshan Ren
- Henan University People’s Hospital, Zhengzhou, China
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qian Li
- Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Shunhua Liu
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qinghua Qi
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaobo Duan
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Bing Mao
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Xin Li
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yuejin Wu
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Lianzhong Zhang
- Henan University People’s Hospital, Zhengzhou, China
- Henan Provincial People’s Hospital, Zhengzhou, China
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15
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Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021; 54:890-901. [PMID: 34390014 PMCID: PMC8435007 DOI: 10.1111/apt.16563] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/08/2021] [Accepted: 07/25/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. AIMS To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. METHODS We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. RESULTS A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. CONCLUSIONS Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.
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Affiliation(s)
- Emily Harding-Theobald
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Jeremy Louissaint
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Bharat Maraj
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Edward Cuaresma
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Whitney Townsend
- Division of Library Sciences, University of Michigan, Ann Arbor, MI, USA
| | | | - Amit G Singal
- Division of Digestive and Liver Diseases, University of Texas Southwestern, Dallas, TX, USA
| | - Grace L Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Anna S Lok
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
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16
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Jaggi A, Mastrodicasa D, Charville GW, Jeffrey RB, Napel S, Patel B. Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma. J Med Imaging (Bellingham) 2021; 8:054501. [PMID: 34514033 DOI: 10.1117/1.jmi.8.5.054501] [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: 01/08/2021] [Accepted: 08/05/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach: In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12 years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results: Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p < 0.00001 ). Conclusions: A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC.
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Affiliation(s)
- Akshay Jaggi
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Domenico Mastrodicasa
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Gregory W Charville
- Stanford University School of Medicine, Department of Pathology, Stanford, California, United States
| | - R Brooke Jeffrey
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Sandy Napel
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Bhavik Patel
- Mayo Clinic Arizona, Department of Radiology, Phoenix, Arizona, United States.,Arizona State University, Ira A. Fulton School of Engineering, Phoenix, Arizona, United States
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17
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Zhang L, Hu J, Hou J, Jiang X, Guo L, Tian L. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection. Abdom Radiol (NY) 2021; 46:3845-3854. [PMID: 33733337 DOI: 10.1007/s00261-021-03034-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a prediction model that combined magnetic resonance images (MRI)-based radiomics features with clinical factors to predict recurrence-free survival (RFS) of hepatocellular carcinoma (HCC) patients treated with surgical resection. METHODS HCC patients treated with surgical resection (n = 153) were randomly divided into training (n = 107) and validation (n = 46) datasets. The volumes of interest were manually outlined around the lesion and additional 2 mm and 5 mm peritumoral areas were created with automated dilatation in MRI to extract tumoral (T) and peritumoral (PT) radiomics features. The radiomics models were constructed using least absolute shrinkage and selection operator Cox regression. The combined model incorporated clinical factors and radiomics features using multivariable Cox regression based on the Akaike information criterion principle. Predictive performance of different models were evaluated by receiver operating characteristic (ROC) curves, decision curves, and calibration curves. RESULTS Among the radiomics models, similar performance was observed in the 2 mm and 5 mm PT models (C-index both 0.657), which were better than the T model or T + PT model (C-index 0.607 and 0.641, respectively) in the validation dataset, whereas the model combined with the three identified clinical risk factors showed the best performance (C-index 0.725). Results of the ROC curves, decision curves, and the calibration curves indicated that the combined model and the derived nomogram had better prediction performance, greater clinical benefits, and fair calibration efficiency. CONCLUSION The prediction model that combined MRI radiomics signatures with clinical factors can effectively predict the prognosis of patients with HCC treated with surgical resection.
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Affiliation(s)
- Ling Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Jianming Hu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Jingyu Hou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xinhua Jiang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Lei Guo
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Li Tian
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [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: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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19
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Fan Y, Yu Y, Wang X, Hu M, Hu C. Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma. BMC Med Imaging 2021; 21:100. [PMID: 34130644 PMCID: PMC8204550 DOI: 10.1186/s12880-021-00633-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 06/04/2021] [Indexed: 02/08/2023] Open
Abstract
Background Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30–60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00633-0.
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Affiliation(s)
- Yanfen Fan
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China.,Institute of Medical Imaging of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China
| | - Yixing Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China.,Institute of Medical Imaging of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China.,Institute of Medical Imaging of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China
| | - Mengjie Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China.,Institute of Medical Imaging of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China. .,Institute of Medical Imaging of Soochow University, Shizi Street 188, Suzhou, 215006, Jiangsu, People's Republic of China.
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20
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Dai H, Lu M, Huang B, Tang M, Pang T, Liao B, Cai H, Huang M, Zhou Y, Chen X, Ding H, Feng ST. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging. Quant Imaging Med Surg 2021; 11:1836-1853. [PMID: 33936969 DOI: 10.21037/qims-20-218] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Microvascular invasion (MVI) has a significant effect on the prognosis of hepatocellular carcinoma (HCC), but its preoperative identification is challenging. Radiomics features extracted from medical images, such as magnetic resonance (MR) images, can be used to predict MVI. In this study, we explored the effects of different imaging sequences, feature extraction and selection methods, and classifiers on the performance of HCC MVI predictive models. Methods After screening against the inclusion criteria, 69 patients with HCC and preoperative gadoxetic acid-enhanced MR images were enrolled. In total, 167 features were extracted from the MR images of each sequence for each patient. Experiments were designed to investigate the effects of imaging sequence, number of gray levels (Ng), quantization algorithm, feature selection method, and classifiers on the performance of radiomics biomarkers in the prediction of HCC MVI. We trained and tested these models using leave-one-out cross-validation (LOOCV). Results The radiomics model based on the images of the hepatobiliary phase (HBP) had better predictive performance than those based on the arterial phase (AP), portal venous phase (PVP), and pre-enhanced T1-weighted images [area under the receiver operating characteristic (ROC) curve (AUC) =0.792 vs. 0.641/0.634/0.620, P=0.041/0.021/0.010, respectively]. Compared with the equal-probability and Lloyd-Max algorithms, the radiomics features obtained using the Uniform quantization algorithm had a better performance (AUC =0.643/0.666 vs. 0.792, P=0.002/0.003, respectively). Among the values of 8, 16, 32, 64, and 128, the best predictive performance was achieved when the Ng was 64 (AUC =0.792 vs. 0.584/0.697/0.677/0.734, P<0.001/P=0.039/0.001/0.137, respectively). We used a two-stage feature selection method which combined the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RFE) gradient boosting decision tree (GBDT), which achieved better stability than and outperformed LASSO, minimum redundancy maximum relevance (mRMR), and support vector machine (SVM)-RFE (stability =0.967 vs. 0.837/0.623/0.390, respectively; AUC =0.850 vs. 0.792/0.713/0.699, P=0.142/0.007/0.003, respectively). The model based on the radiomics features of HBP images using the GBDT classifier showed a better performance for the preoperative prediction of MVI compared with logistic regression (LR), SVM, and random forest (RF) classifiers (AUC =0.895 vs. 0.850/0.834/0.884, P=0.558/0.229/0.058, respectively). With the optimal combination of these factors, we established the best model, which had an AUC of 0.895, accuracy of 87.0%, specificity of 82.5%, and sensitivity of 93.1%. Conclusions Imaging sequences, feature extraction and selection methods, and classifiers can have a considerable effect on the predictive performance of radiomics models for HCC MVI.
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Affiliation(s)
- Houjiao Dai
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Shenzhen University Clinical Research Center for Neurological Diseases, Shenzhen University General Hospital, Shenzhen, China
| | - Minhua Lu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Shenzhen University Clinical Research Center for Neurological Diseases, Shenzhen University General Hospital, Shenzhen, China
| | - Mimi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tiantian Pang
- School of Computer Science and Software Engineering, Jilin University, Changchun, China
| | - Bing Liao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mengqi Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongjin Zhou
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen, China
| | - Xin Chen
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Huijun Ding
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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21
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Sambri A, Caldari E, Fiore M, Zucchini R, Giannini C, Pirini MG, Spinnato P, Cappelli A, Donati DM, De Paolis M. Margin Assessment in Soft Tissue Sarcomas: Review of the Literature. Cancers (Basel) 2021; 13:cancers13071687. [PMID: 33918457 PMCID: PMC8038240 DOI: 10.3390/cancers13071687] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary Many classifications to assess margins status for soft tissue sarcomas are reported in the literature. Most of the series are heterogeneous and variable in size, making it difficult to compare results from study to study. Thus, which is the best way to assess margins in order to predict the risk of local recurrence is still debated. The aim of this narrative review is to provide a comprehensive assessment of the literature on margins, and to highlight the need for a uniform description of the margin status for patients with soft tissue sarcomas (STS). Abstract Adequacy of margins must take into consideration both the resection margin width (quantity) and anatomic barrier (quality). There are several classification schemes for reporting surgical resection margin status for soft tissue sarcomas (STS). Most of the studies regarding treatment outcomes in STS included all histologic grades and histological subtypes, which include infiltrative and non-infiltrative subtypes and are very heterogeneous in terms of both histologic characteristics and treatment modalities (adjuvant treatments or not). This lack of consistency makes it difficult to compare results from study to study. Therefore, there is a great need for evidence-based standardization concerning the width of resection margins. The aim of this narrative review is to provide a comprehensive assessment of the literature on margins, and to highlight the need for a uniform description of the margin status for patients with STS. Patient cases should be discussed at multidisciplinary tumor boards and treatments should be individualized to clinical and demographic characteristics, which must include also a deep knowledge of specific histotypes behaviors, particularly infiltrative ones.
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Affiliation(s)
- Andrea Sambri
- Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy;
- IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy; (E.C.); (M.G.P.); (A.C.); (M.D.P.)
- Correspondence:
| | - Emilia Caldari
- IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy; (E.C.); (M.G.P.); (A.C.); (M.D.P.)
| | - Michele Fiore
- IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy; (M.F.); (R.Z.); (C.G.); (P.S.)
| | - Riccardo Zucchini
- IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy; (M.F.); (R.Z.); (C.G.); (P.S.)
| | - Claudio Giannini
- IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy; (M.F.); (R.Z.); (C.G.); (P.S.)
| | - Maria Giulia Pirini
- IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy; (E.C.); (M.G.P.); (A.C.); (M.D.P.)
| | - Paolo Spinnato
- IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy; (M.F.); (R.Z.); (C.G.); (P.S.)
| | - Alberta Cappelli
- IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy; (E.C.); (M.G.P.); (A.C.); (M.D.P.)
| | - Davide Maria Donati
- Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy;
- IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy; (M.F.); (R.Z.); (C.G.); (P.S.)
| | - Massimiliano De Paolis
- IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy; (E.C.); (M.G.P.); (A.C.); (M.D.P.)
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22
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Zhou W, Jian W, Cen X, Zhang L, Guo H, Liu Z, Liang C, Wang G. Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks. Front Oncol 2021; 11:588010. [PMID: 33854959 PMCID: PMC8040801 DOI: 10.3389/fonc.2021.588010] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 01/08/2021] [Indexed: 12/24/2022] Open
Abstract
Background and Purpose It is extremely important to predict the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) before surgery, which is a key predictor of recurrence and helps determine the treatment strategy before liver resection or liver transplantation. In this study, we demonstrate that a deep learning approach based on contrast-enhanced MR and 3D convolutional neural networks (CNN) can be applied to better predict MVI in HCC patients. Materials and Methods This retrospective study included 114 consecutive patients who were surgically resected from October 2012 to October 2018 with 117 histologically confirmed HCC. MR sequences including 3.0T/LAVA (liver acquisition with volume acceleration) and 3.0T/e-THRIVE (enhanced T1 high resolution isotropic volume excitation) were used in image acquisition of each patient. First, numerous 3D patches were separately extracted from the region of each lesion for data augmentation. Then, 3D CNN was utilized to extract the discriminant deep features of HCC from contrast-enhanced MR separately. Furthermore, loss function for deep supervision was designed to integrate deep features from multiple phases of contrast-enhanced MR. The dataset was divided into two parts, in which 77 HCCs were used as the training set, while the remaining 40 HCCs were used for independent testing. Receiver operating characteristic curve (ROC) analysis was adopted to assess the performance of MVI prediction. The output probability of the model was assessed by the independent student's t-test or Mann-Whitney U test. Results The mean AUC values of MVI prediction of HCC were 0.793 (p=0.001) in the pre-contrast phase, 0.855 (p=0.000) in arterial phase, and 0.817 (p=0.000) in the portal vein phase. Simple concatenation of deep features using 3D CNN derived from all the three phases improved the performance with the AUC value of 0.906 (p=0.000). By comparison, the proposed deep learning model with deep supervision loss function produced the best results with the AUC value of 0.926 (p=0.000). Conclusion A deep learning framework based on 3D CNN and deeply supervised net with contrast-enhanced MR could be effective for MVI prediction.
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Affiliation(s)
- Wu Zhou
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wanwei Jian
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaoping Cen
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lijuan Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hui Guo
- Department of Optometry, Guangzhou Aier Eye Hospital, Jinan University, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Guangyi Wang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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23
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Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021; 82:295-305. [PMID: 33714190 PMCID: PMC8035300 DOI: 10.1016/j.ejmp.2021.02.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/13/2021] [Accepted: 02/19/2021] [Indexed: 02/07/2023] Open
Abstract
This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients' electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544-0.621), 0.629 (95%CI: 0.601-0.643), 0.581 (95%CI: 0.553-0.613) and 0.650 (95%CI: 0.635-0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Xinzhou Guo
- Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
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24
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Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6685723. [PMID: 33506029 PMCID: PMC7810556 DOI: 10.1155/2021/6685723] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Purpose To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.
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25
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Nebbia G, Zhang Q, Arefan D, Zhao X, Wu S. Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics. J Digit Imaging 2020; 33:1376-1386. [PMID: 32495126 PMCID: PMC7728938 DOI: 10.1007/s10278-020-00353-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Microvascular invasion (mVI) is the most significant independent predictor of recurrence for hepatocellular carcinoma (HCC), but its pre-operative assessment is challenging. In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver MRI scans for 99 patients who were diagnosed with HCC. These patients received surgery and pathology-confirmed diagnosis of mVI. We extracted radiomics features from manually segmented HCC regions and built machine learning classifiers to predict mVI status. We compared the performance of such classifiers when built on five MRI sequences used both individually and combined. We investigated the effects of using features extracted from the tumor region only, the peritumoral marginal region only, and the combination of the two. We used the area under the receiver operating characteristic curve (AUC) and accuracy as performance metrics. By combining features extracted from multiple MRI sequences, AUCs are 86.69%, 84.62%, and 84.19% when features are extracted from the tumor only, the peritumoral region only, and the combination of the two, respectively. For tumor-extracted features, the T2 sequence (AUC = 80.84%) and portal venous sequence (AUC = 79.22%) outperform other MRI sequences in single-sequence-based models and their combination yields the highest AUC of 86.69% for mVI status prediction. Our results show promise in predicting mVI from pre-operative liver MRI scans and indicate that information from multi-parametric MRI sequences is complementary in identifying mVI.
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Affiliation(s)
- Giacomo Nebbia
- Intelligent Systems Program, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA
| | - Qian Zhang
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dooman Arefan
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA
| | - Xinxiang Zhao
- Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, 374 Dianmian Road, Wuhua District, Kunming, 650101, Yunnan, China.
| | - Shandong Wu
- Intelligent Systems Program, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA.
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA.
- Department of Bioengineering, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3362 Fifth Ave, Rm. 130, Pittsburgh, PA, 15213, USA.
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26
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Haak HE, Maas M, Trebeschi S, Beets-Tan RGH. Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye. Front Oncol 2020; 10:537532. [PMID: 33117678 PMCID: PMC7578261 DOI: 10.3389/fonc.2020.537532] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 09/02/2020] [Indexed: 12/29/2022] Open
Abstract
MR imaging (MRI) is now part of the standard work up of patients with rectal cancer. Restaging MRI has been traditionally used to plan the surgical approach. Its role has recently increased and been adopted as a valuable tool to assist the clinical selection of clinical (near) complete responders for organ preserving treatment. Recently several studies have addressed new imaging biomarkers that combined with morphological provides a comprehensive picture of the tumor. Diffusion-weighted MRI (DWI) has entered the clinics and proven useful for response assessment after chemoradiotherapy. Other functional (quantitative) MRI technologies are on the horizon including artificial intelligence modeling. This narrative review provides an overview of recent advances in rectal cancer (re)staging by imaging with a specific focus on response prediction and evaluation of neoadjuvant treatment response. Furthermore, directions are given for future research.
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Affiliation(s)
- Hester E Haak
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,Department of Surgery, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Monique Maas
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
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27
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Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol 2020; 196:888-899. [PMID: 32296901 PMCID: PMC7498486 DOI: 10.1007/s00066-020-01615-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 03/20/2020] [Indexed: 12/15/2022]
Abstract
Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.
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Affiliation(s)
- Constantin Dreher
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1–3, 68167 Mannheim, Germany
| | - Philipp Linde
- Department of Radiation Oncology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1–3, 68167 Mannheim, Germany
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
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Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020; 40:2050-2063. [PMID: 32515148 PMCID: PMC7496410 DOI: 10.1111/liv.14555] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/28/2020] [Accepted: 05/29/2020] [Indexed: 02/05/2023]
Abstract
Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Hanyu Jiang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Dongsheng Gu
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Meng Niu
- Department of Interventional RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangChina
| | - Fangfang Fu
- Department of Medical ImagingHenan Provincial People’s HospitalZhengzhouHenanChina
- Department of Medical ImagingPeople’s Hospital of Zhengzhou University. ZhengzhouHenanChina
| | - Yuqi Han
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Jie Tian
- Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijingChina
- Beijing Key Laboratory of Molecular ImagingBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineSchool of MedicineBeihang UniversityBeijingChina
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of EducationSchool of Life Science and TechnologyXidian UniversityXi’anShaanxiChina
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Bakr S, Gevaert O, Patel B, Kesselman A, Shah R, Napel S, Kothary N. Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images. Radiol Imaging Cancer 2020; 2:e190062. [PMID: 32550600 DOI: 10.1148/rycan.2020190062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 01/06/2020] [Accepted: 01/22/2020] [Indexed: 12/14/2022]
Abstract
Purpose To evaluate interreader agreement in annotating semantic features on preoperative CT images to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Materials and Methods Preoperative, contrast material-enhanced triphasic CT studies from 89 patients (median age, 64 years; age range, 36-85 years; 70 men) who underwent hepatic resection between 2008 and 2017 for a solitary HCC were reviewed. Three radiologists annotated CT images obtained during the arterial and portal venous phases, independently and in consensus, with features associated with MVI reported by other investigators. The assessed factors were the presence or absence of discrete internal arteries, hypoattenuating halo, tumor-liver difference, peritumoral enhancement, and tumor margin. Testing also included previously proposed MVI signatures: radiogenomic venous invasion (RVI) and two-trait predictor of venous invasion (TTPVI), using single-reader and consensus annotations. Cohen (two-reader) and Fleiss (three-reader) κ and the bootstrap method were used to analyze interreader agreement and differences in model performance, respectively. Results Of HCCs assessed, 32.6% (29 of 89) had MVI at histopathologic findings. Two-reader agreement, as assessed by pairwise Cohen κ statistics, varied as a function of feature and imaging phase, ranging from 0.02 to 0.6; three-reader Fleiss κ varied from -0.17 to 0.56. For RVI and TTPVI, the best single-reader performance had sensitivity and specificity of 52% and 77% and 67% and 74%, respectively. In consensus, the sensitivity and specificity for the RVI and TTPVI signatures were 59% and 67% and 70% and 62%, respectively. Conclusion Interreader variability in semantic feature annotation remains a challenge and affects the reproducibility of predictive models for preoperative detection of MVI in HCC.Supplemental material is available for this article.© RSNA, 2020.
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Affiliation(s)
- Shaimaa Bakr
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Olivier Gevaert
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Bhavik Patel
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Andrew Kesselman
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Rajesh Shah
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Sandy Napel
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
| | - Nishita Kothary
- Departments of Electrical Engineering (S.B.) and Radiology (O.G., B.P., R.S., S.N.), Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305-5450; Department of Radiology, State University of New York Downstate Medical Center, Brooklyn, NY (A.K.); and Department of Radiology, Stanford School of Medicine, Stanford, Calif (N.K.)
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Sun Y, Bai H, Xia W, Wang D, Zhou B, Zhao X, Yang G, Xu L, Zhang W, Liu P, Xu J, Meng S, Liu R, Gao X. Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI. J Magn Reson Imaging 2020; 52:1083-1090. [PMID: 32233054 DOI: 10.1002/jmri.27143] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/04/2020] [Accepted: 03/04/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In unresectable hepatocellular carcinoma (HCC), methods to predict patients at increased risk of progression are required. PURPOSE To investigate the feasibility of radiomics model in predicting early progression of unresectable HCC after transcatheter arterial chemoembolization (TACE) therapy using preoperative multiparametric magnetic resonance imaging (MP-MRI). STUDY TYPE Retrospective. POPULATION A total of 84 patients with BCLC B stage HCC from one medical center. According to the modified response evaluation criteria in solid tumors, patients who progressed at 6 months after TACE therapy were assigned as the progressive disease (PD) group (n = 32). Patients whose MRI was performed on four devices were divided into a training cohort (n = 67). Patients whose MRI was performed on other than the previous four devices were used as the testing set (n = 17). FIELD STRENGTH/SEQUENCE 3.0T, 1.5T axial T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI, b = 0, 500 s/mm2 ), and apparent diffusion coefficient (ADC) ASSESSMENT: PD was confirmed via imaging studies with MRI. Risk factors, including age, alpha fetoprotein (AFP), size, and radiomic-related features of PD were assessed. In addition, the discrimination ability of each radiomics signature was tested on an independent testing set. STATISTICAL TESTS The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and testing sets. The results indicated that the MP-MRI model achieved the greatest benefit. RESULTS In the testing set, the model based on DWI features presented an AUC of (b = 0, 0.786; b = 500, 0.729), followed by T2 WI features (0.729) and ADC (0.714). The AUC of the MP-MRI signature was increased to 0.800 compared to any single MRI signature. The multivariate logistic analysis identified the radiomics signature as independent parameters of PD, while clinical information such as age, AFP, size, etc., had no significance in the PD group. DATA CONCLUSION Preoperative MP-MRI has the potential to predict the outcome of TACE therapy for unresectable HCC. In addition, these image features may be complementary to the current staging systems of HCC patients. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 3. J. Magn. Reson. Imaging 2020;52:1083-1090.
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Affiliation(s)
- Yuejun Sun
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Honglin Bai
- University of Science and Technology of China, Hefei, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Beijing, China
| | - Wei Xia
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Beijing, China
| | - Dong Wang
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bo Zhou
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xingyu Zhao
- University of Science and Technology of China, Hefei, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Beijing, China
| | - Guowei Yang
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ligang Xu
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Pingping Liu
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jiacheng Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Siyu Meng
- Department of Electro-Optical Engineering, Changchun University of Science and Technology, Changchun, China
| | - Rong Liu
- Department of Interventional Radiology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Institution of Medical Imaging, Shanghai, China
| | - Xin Gao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Beijing, China
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Dong Y, Zhou L, Xia W, Zhao XY, Zhang Q, Jian JM, Gao X, Wang WP. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images. Front Oncol 2020; 10:353. [PMID: 32266138 PMCID: PMC7096379 DOI: 10.3389/fonc.2020.00353] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross- and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.
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Affiliation(s)
- Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liu Zhou
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Wei Xia
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Xing-Yu Zhao
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Qi Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun-Ming Jian
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Wen-Ping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
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Rubin DL, Ugur Akdogan M, Altindag C, Alkim E. ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging. ACTA ACUST UNITED AC 2020; 5:170-183. [PMID: 30854455 PMCID: PMC6403025 DOI: 10.18383/j.tom.2018.00055] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.
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Affiliation(s)
- Daniel L Rubin
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Mete Ugur Akdogan
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Cavit Altindag
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Emel Alkim
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
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Wang XH, Long LH, Cui Y, Jia AY, Zhu XG, Wang HZ, Wang Z, Zhan CM, Wang ZH, Wang WH. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer 2020; 122:978-985. [PMID: 31937925 PMCID: PMC7109104 DOI: 10.1038/s41416-019-0706-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/10/2019] [Accepted: 10/08/2019] [Indexed: 12/17/2022] Open
Abstract
Background Recurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is difficult to select candidates for additional therapy. Methods A total of 201 patients with HCC who were followed up for at least 5 years after curative hepatectomy were enrolled in this retrospective, multicentre study. A total of 3144 radiomics features were extracted from preoperative MRI. The random forest method was used for radiomics signature building, and five-fold cross-validation was applied. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. Results Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on the 5-year survival after surgery. The 30 most survival-related radiomics features were selected for the radiomics signature. Preoperative AFP and AST were integrated into the model as independent clinical risk factors. The model demonstrated good calibration and satisfactory discrimination, with a mean AUC of 0.9804 and 0.7578 in the training and validation sets, respectively. Conclusions This radiomics model is a valid method to predict 5-year survival in patients with HCC and may be used to identify patients for clinical trials of perioperative therapies and for additional surveillance.
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Affiliation(s)
- Xiao-Hang Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Liu-Hua Long
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Yong Cui
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Angela Y Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Xiang-Gao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Hong-Zhi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Zhi Wang
- Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
| | | | - Zhao-Hai Wang
- Department of Hepatobiliary Surgery, The Fifth Medical Center of Chinese PLA General Hospital, Beijing Institute of Infectious Diseases, Beijing, China.
| | - Wei-Hu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
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Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, Yang H, Ammari S, Saenger Y, Rousseau H, Zhao B, Schwartz LH, Dercle L. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol 2020; 30:558-570. [PMID: 31444598 DOI: 10.1007/s00330-019-06347-w] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/12/2019] [Accepted: 06/27/2019] [Indexed: 02/08/2023]
Abstract
PURPOSE To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans. MATERIAL AND METHODS We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated. RESULTS Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: DeltaV-A_DWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement. CONCLUSION A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk. KEY POINTS • In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.
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Affiliation(s)
- Fatima-Zohra Mokrane
- Radiology Department, Rangueil University Hospital, Toulouse, France.
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA.
| | - Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Adrien Vavasseur
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Philippe Otal
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Jean-Marie Peron
- Hepatology Department, Purpan University Hospital, Toulouse, France
| | - Lyndon Luk
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Hao Yang
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Samy Ammari
- Service de Radiologie, Gustave-Roussy, Université Paris-Saclay, Villejuif, France
| | - Yvonne Saenger
- Department of Medicine, Division of Hematology/Oncology, Columbia University Medical Center/New York Presbyterian, New York, NY, USA
| | - Herve Rousseau
- Radiology Department, Rangueil University Hospital, Toulouse, France
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
| | - Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA
- INSERM U1015, Gustave Roussy Institute, Université Paris-Saclay, F-94805, Villejuif, France
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Bulens P, Couwenberg A, Intven M, Debucquoy A, Vandecaveye V, Van Cutsem E, D'Hoore A, Wolthuis A, Mukherjee P, Gevaert O, Haustermans K. Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiother Oncol 2020; 142:246-252. [PMID: 31431368 PMCID: PMC6997038 DOI: 10.1016/j.radonc.2019.07.033] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/19/2019] [Accepted: 07/28/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection. MATERIALS AND METHODS Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation. RESULTS 21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model. CONCLUSION Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.
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Affiliation(s)
- Philippe Bulens
- Department of Radiation Oncology, University Hospitals Leuven, Belgium
| | - Alice Couwenberg
- Department of Radiation Oncology, University Medical Center Utrecht, The Netherlands
| | - Martijn Intven
- Department of Radiation Oncology, University Medical Center Utrecht, The Netherlands
| | | | | | - Eric Van Cutsem
- Department of Digestive Oncology, University Hospitals Leuven, Belgium
| | - André D'Hoore
- Department of Abdominal Surgery, University Hospitals Leuven, Belgium
| | - Albert Wolthuis
- Department of Abdominal Surgery, University Hospitals Leuven, Belgium
| | - Pritam Mukherjee
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, USA
| | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals Leuven, Belgium.
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Zhang R, Xu L, Wen X, Zhang J, Yang P, Zhang L, Xue X, Wang X, Huang Q, Guo C, Shi Y, Niu T, Chen F. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Quant Imaging Med Surg 2019; 9:1503-1515. [PMID: 31667137 DOI: 10.21037/qims.2019.09.07] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background We aimed to develop and validate a nomogram combining bi-regional radiomics features from multimodal magnetic resonance imaging (MRI) and clinicoradiological characteristics to preoperatively predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Methods A total of 267 HCC patients were divided into training (n=194) and validation (n=73) cohorts according to MRI data. Bi-regional features were extracted from whole tumors and peritumoral regions in multimodal MRI. The minimum redundancy maximum relevance (mRMR) algorithm was applied to select features and build signatures. The predictive performance of the optimal radiomics signature was further evaluated within subgroups defined by tumor size and alpha fetoprotein (AFP) level. Then, a radiomics nomogram including the optimal radiomics signature, radiographic descriptors, and clinical variables was developed using multivariable regression. The nomogram performance was evaluated based on its discrimination, calibration, and clinical utility. Results The fusion radiomics signature derived from triphasic dynamic contrast-enhanced (DCE) MR images can effectively classify MVI and non-MVI HCC patients, with an AUC of 0.784 (95% CI: 0.719-0.840) in the training cohort and 0.820 (95% CI: 0.713-0.900) in the validation cohort. The fusion radiomics signature also performed well in the subgroups defined by the two risk factors, respectively. The nomogram, consisting of the fusion radiomics signature, arterial peritumoral enhancement, and AFP level, outperformed the clinicoradiological prediction model in the validation cohort (AUCs: 0.858 vs. 0.729; P=0.022), fitting well in the calibration curves (P>0.05). Decision curves confirmed the clinical utility of the nomogram. Conclusions The radiomics nomogram can serve as a visual predictive tool for MVI in HCCs, and thus assist clinicians in selecting optimal treatment strategies to improve clinical outcomes.
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Affiliation(s)
- Rui Zhang
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Lei Xu
- Institute of Translational Medicine, College of Medicine, Zhejiang University, Hangzhou 310058, China.,Department of Radiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Xue Wen
- Department of Pathology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jiahui Zhang
- Department of Radiology, Hangzhou Third Hospital, Hangzhou 310009, China
| | - Pengfei Yang
- Institute of Translational Medicine, College of Medicine, Zhejiang University, Hangzhou 310058, China.,Department of Radiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Lixia Zhang
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Xing Xue
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Xiaoli Wang
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Qiang Huang
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Chuangen Guo
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Yanjun Shi
- Department of Hepatobiliary and Pancreas Surgery, the Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China
| | - Tianye Niu
- Institute of Translational Medicine, College of Medicine, Zhejiang University, Hangzhou 310058, China.,Department of Radiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Feng Chen
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
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Abstract
Radiomics is an emerging field which extracts quantitative radiology data from medical images and explores their correlation with clinical outcomes in a non-invasive manner. This review aims to assess whether radiomics is a useful and reproducible method for clinical management of hepatocellular carcinoma (HCC) by reviewing the strengths and weaknesses of current radiomics literature pertaining specifically to HCC. From an initial set of 48 articles recovered through database searches, 23 articles were retained to be included in this review after full screening. Among these 23 studies, 7 used a radiomics approach in magnetic resonance imaging (MRI). Only two studies applied radiomics to positron emission tomography-computed tomography (PET-CT). In the remaining 14 articles, a radiomics analysis was performed on computed tomography (CT). Eight studies dealt with the relationship between biological signatures and imaging findings, and can be classified as radiogenomic studies. For each study included in our review, we computed a Radiomics Quality Score (RQS) as proposed by Lambin et al. We found that the RQS (mean ± standard deviation) was 8.35 ± 5.38 (out of a possible maximum value of 36). Although these scores are fairly low, and radiomics has not yet reached clinical utility in HCC, it is important to underscore the fact that these early studies pave the way for the radiomics field with a focus on HCC. Radiomics is still a very young field, and is far from being mature, but it remains a very promising technology for the future for developing adequate personalized treatment as a non-invasive approach, for complementing or replacing tumor biopsies, as well as for developing novel prognostic biomarkers in HCC patients.
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Ni M, Zhou X, Lv Q, Li Z, Gao Y, Tan Y, Liu J, Liu F, Yu H, Jiao L, Wang G. Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the best model? Cancer Imaging 2019; 19:60. [PMID: 31455432 PMCID: PMC6712704 DOI: 10.1186/s40644-019-0249-x] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/14/2019] [Indexed: 12/16/2022] Open
Abstract
Objectives To explore the feasibility of diagnosing microvascular invasion (MVI) with radiomics, to compare the diagnostic performance of different models established by each method, and to determine the best diagnostic model based on radiomics. Methods A retrospective analysis was conducted with 206 cases of hepatocellular carcinoma (HCC) confirmed through surgery and pathology in our hospital from June 2015 to September 2018. Among the samples, 88 were MVI-positive, and 118 were MVI-negative. The radiomics analysis process included tumor segmentation, feature extraction, data preprocessing, dimensionality reduction, modeling and model evaluation. Results A total of 1044 sets of texture feature parameters were extracted, and 21 methods were used for the radiomics analysis. All research methods could be used to diagnose MVI. Of all the methods, the LASSO+GBDT method had the highest accuracy, the LASSO+RF method had the highest sensitivity, the LASSO+BPNet method had the highest specificity, and the LASSO+GBDT method had the highest AUC. Through Z-tests of the AUCs, LASSO+GBDT, LASSO+K-NN, LASSO+RF, PCA + DT, and PCA + RF had Z-values greater than 1.96 (p<0.05). The DCA results showed that the LASSO + GBDT method was better than the other methods when the threshold probability was greater than 0.22. Conclusions Radiomics can be used for the preoperative, noninvasive diagnosis of MVI, but different dimensionality reduction and modeling methods will affect the diagnostic performance of the final model. The model established with the LASSO+GBDT method had the optimal diagnostic performance and the greatest diagnostic value for MVI.
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Affiliation(s)
- Ming Ni
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
| | - Qian Lv
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Yongqi Tan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Jihua Liu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Fang Liu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Haiyang Yu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Linlin Jiao
- Intervention Medical Center, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
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Ma X, Wei J, Gu D, Zhu Y, Feng B, Liang M, Wang S, Zhao X, Tian J. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 2019; 29:3595-3605. [PMID: 30770969 DOI: 10.1007/s00330-018-5985-y] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/05/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHODS The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics. RESULTS Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357. CONCLUSIONS The above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment. KEY POINTS • No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging. • The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone. • Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.
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Affiliation(s)
- Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China
| | - Jingwei Wei
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yongjian Zhu
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, People's Republic of China.
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, People's Republic of China.
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40
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Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 2019; 9:1303-1322. [PMID: 30867832 PMCID: PMC6401507 DOI: 10.7150/thno.30309] [Citation(s) in RCA: 566] [Impact Index Per Article: 94.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/10/2019] [Indexed: 12/14/2022] Open
Abstract
Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Cheng Fang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
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Chen S, Feng S, Wei J, Liu F, Li B, Li X, Hou Y, Gu D, Tang M, Xiao H, Jia Y, Peng S, Tian J, Kuang M. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 2019; 29:4177-4187. [PMID: 30666445 DOI: 10.1007/s00330-018-5986-x] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Revised: 11/22/2018] [Accepted: 12/18/2018] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Immunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0-2 vs. 3-4) in HCC. MATERIALS AND METHODS The study included 207 (training cohort: n = 150; validation cohort: n = 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model). RESULTS The combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855-0.953) vs. 0.823 (95% CI 0.747-0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0·926 (95% CI 0·884-0·967) vs. 0·904 (95% CI 0·855-0·953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement. CONCLUSION The MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions. KEY POINTS • Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma. • Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore. • We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.
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Affiliation(s)
- Shuling Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Shiting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fei Liu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bin Li
- Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Xin Li
- GE HealthCare China, Shanghai, 200000, China
| | - Yang Hou
- Department of Mathematics, Jinan University, Guangzhou, 510632, China
| | - Dongsheng Gu
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mimi Tang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Han Xiao
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Yingmei Jia
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Sui Peng
- Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.,Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China. .,Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
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Napel S, Mu W, Jardim‐Perassi BV, Aerts HJWL, Gillies RJ. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 2018; 124:4633-4649. [PMID: 30383900 PMCID: PMC6482447 DOI: 10.1002/cncr.31630] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/11/2018] [Accepted: 07/17/2018] [Indexed: 11/07/2022]
Abstract
Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.
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Affiliation(s)
- Sandy Napel
- Department of RadiologyStanford UniversityStanfordCalifornia
| | - Wei Mu
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
| | | | - Hugo J. W. L. Aerts
- Dana‐Farber Cancer Institute, Department of Radiology, Brigham and Women’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Robert J. Gillies
- Department of Cancer PhysiologyH. Lee Moffitt Cancer CenterTampaFlorida
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Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, Ding ZB, Ji Y, Rao SX, Zhou J, Fan J, Wang XY, Gao Q. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer 2018; 18:1148. [PMID: 30463529 PMCID: PMC6249916 DOI: 10.1186/s12885-018-5024-z] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 10/31/2018] [Indexed: 12/18/2022] Open
Abstract
Background Radiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC). Methods A total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients. A rad-score was generated by using the least absolute shrinkage and selection operator (lasso) logistic model. Kaplan-Meier and Cox’s hazard regression analyses were used to evaluate the prognostic significance of the rad-score. Final nomograms predicting recurrence and survival of solitary HCC patients were established based on the rad-score and clinicopathological factors. C-index and calibration statistics were used to assess the performance of nomograms. Results Six potential radiomics features were selected out of 110 texture features to formulate the rad-score. Low rad-score positively correlated with aggressive tumor phenotypes, like larger tumor size and vascular invasion. Meanwhile, low rad-score was significantly associated with increased recurrence and reduced survival. In addition, multivariate analysis identified the rad-score as an independent prognostic factor (recurrence: Hazard ratio (HR): 2.472, 95% confident interval (CI): 1.339–4.564, p = 0.004;survival: HR: 1.558, 95%CI: 1.022–2.375, p = 0.039). Notably, the nomogram integrating rad-score had a better prognostic performance as compared with traditional staging systems. These results were further confirmed in the validation cohort. Conclusions The preoperative CT image based rad-score was an independent prognostic factor for the postoperative outcome of solitary HCC patients. This score may be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients. Electronic supplementary material The online version of this article (10.1186/s12885-018-5024-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo-Hao Zheng
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Long-Zi Liu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Zhi-Zhi Zhang
- Department of Hematology, Shanghai Jiao Tong University School of Medicine Affiliated Tongren Hospital, Shanghai, China
| | - Jie-Yi Shi
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Liang-Qing Dong
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ling-Yu Tian
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Zhen-Bin Ding
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Sheng-Xiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.,Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Xiao-Ying Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. .,State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, People's Republic of China.
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44
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Jeong WK, Jamshidi N, Felker ER, Raman SS, Lu DS. Radiomics and radiogenomics of primary liver cancers. Clin Mol Hepatol 2018; 25:21-29. [PMID: 30441889 PMCID: PMC6435966 DOI: 10.3350/cmh.2018.1007] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 08/16/2018] [Indexed: 02/07/2023] Open
Abstract
Concurrent advancements in imaging and genomic biomarkers have created opportunities to identify non-invasive imaging surrogates of molecular phenotypes. In order to develop such imaging surrogates radiomics and radiogenomics/imaging genomics will be necessary; there has been consistent progress in these fields for primary liver cancers. In this article we evaluate the current status of the field specifically with regards to hepatocellular carcinoma and intrahepatic cholangiocarcinoma, highlighting some of the up and coming results that were presented at the annual Radiological Society of North America Conference in 2017. There are an increasing number of studies in this area with a bias towards quantitative feature measurement, which is expected to benefit reproducibility of the findings and portends well for the future development of biomarkers for diagnosis, prognosis, and treatment response assessment. We review some of the advancements and look forward to some of the exciting future applications that are anticipated as the field develops.
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Affiliation(s)
- Woo Kyoung Jeong
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Neema Jamshidi
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ely Richard Felker
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven Satish Raman
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - David Shinkuo Lu
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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45
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Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, Zheng H, Benson JA, Zhang W, Leung ANC, Kadoch M, Hoang CD, Shrager J, Quon A, Rubin DL, Plevritis SK, Napel S. A radiogenomic dataset of non-small cell lung cancer. Sci Data 2018; 5:180202. [PMID: 30325352 PMCID: PMC6190740 DOI: 10.1038/sdata.2018.202] [Citation(s) in RCA: 139] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 07/26/2018] [Indexed: 11/09/2022] Open
Abstract
Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
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Affiliation(s)
- Shaimaa Bakr
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sebastian Echegaray
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kelsey Ayers
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mu Zhou
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Majid Shafiq
- Department of Medicine, Johns Hopkins University, 733 N Broadway, Baltimore, MD 21205, USA
| | - Hong Zheng
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jalen Anthony Benson
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiruo Zhang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ann N C Leung
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Kadoch
- Department of Radiology, University of California Davis, Sacramento, CA 95817, USA
| | - Chuong D Hoang
- Thoracic and GI Oncology Branch, National Institutes of Health/National Cancer Institute, MD 20892, USA
| | - Joseph Shrager
- Stanford School of Medicine, Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford, CA 94305, USA.,VA Palo Alto Health Care System, CA 94304, USA
| | - Andrew Quon
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sylvia K Plevritis
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
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