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Gong T, Gao Y, Li H, Wang J, Li Z, Yuan Q. Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI. Abdom Radiol (NY) 2025:10.1007/s00261-025-04965-1. [PMID: 40448847 DOI: 10.1007/s00261-025-04965-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2025] [Revised: 04/18/2025] [Accepted: 04/20/2025] [Indexed: 06/02/2025]
Abstract
Rectal cancer (RC) is one of the most common malignant tumors of the digestive system and has an alarmingly high incidence and mortality rate globally. Compared to conventional imaging examinations, radiomics can extract quantitative features that reflect tumor heterogeneity and mine data from medical images. In this review, we discuss the potential value of multimodal MRI-based radiomics in the diagnosis and treatment of RC, with a special emphasis on the role of peritumoral tissue characteristics in clinical decision-making. Existing studies have shown that a radiomics model integrating intratumoral and peritumoral characteristics has good application prospects in RC staging evaluation, efficacy prediction, metastasis monitoring, recurrence early warning, and prognosis judgment. At the same time, this paper also objectively analyzes the existing methodological limitations in this field, including insufficient data standardization, inadequate model validation, limited sample size and poor reproducibility of results. By combining existing evidence, this review aimed to enhance the attention of clinicians and radiologists on the characteristics of peritumoral tissues and promote the translational application of radiomics technology in the individualized treatment of RC.
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Affiliation(s)
- Tingting Gong
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Ying Gao
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - He Li
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Jianqiu Wang
- The Second Affiliated Hospital of Jilin University, Jilin Province, China
| | - Zili Li
- Jilin Province Cancer Hospital, Jilin Province, China.
| | - Qinghai Yuan
- The Second Affiliated Hospital of Jilin University, Jilin Province, China.
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Gong X, Ye Z, Shen Y, Song B. Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction. Eur Radiol 2025:10.1007/s00330-025-11463-x. [PMID: 40045072 DOI: 10.1007/s00330-025-11463-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 12/19/2024] [Accepted: 01/28/2025] [Indexed: 03/17/2025]
Abstract
Rectal cancer (RC) is one of the major health challenges worldwide. Accurate staging, restaging, invasiveness assessment, and treatment efficacy evaluation are crucial for its clinical management. Magnetic resonance imaging (MRI) plays a significant role in these processes. However, standard MRI techniques, including T2-weighted and diffusion-weighted imaging, have uncertainties in identifying early-stage tumors, high-risk nodules, extramural vascular invasion, and treatment efficacy, potentially leading to inappropriate treatment. Recent advances suggest that the integration of traditional MRI methods, including diffusion-weighted imaging, opposed-phase or contrast-enhanced T1-weighted imaging, as well as emerging synthetic MRI, could address these challenges. Additionally, improvements in imaging technology have spurred research into advanced functional MRI techniques such as diffusion kurtosis imaging and amide proton transfer weighted MRI, yielding promising results in RC assessment. Total neoadjuvant therapy has emerged as a new treatment paradigm for locally advanced RC, with neoadjuvant immunotherapy and chemotherapy offering viable alternatives to neoadjuvant chemoradiotherapy. However, the lack of standards for the early prediction of patient survival and tumor response to neoadjuvant therapy highlights a critical unmet need in matching therapies to suitable patients. Furthermore, organ preservation strategies after neoadjuvant therapy provide personalized options based on tumor response and patient preferences, yet traditional MRI assessments show significant variability. Radiomics and artificial intelligence hold promise for revealing complex patterns in MRI images associated with patient prognosis and treatment response. This review provides an overview of current MRI advancements in RC assessment and emphasizes how future research can refine tailored treatment strategies to improve patient outcomes. KEY POINTS: Question The accurate diagnosis of early-stage rectal tumors, high-risk nodules, treatment responses, and the early prediction of patient survival and therapeutic outcomes remain an unmet need. Findings Visual MRI has improved staging, restaging, and invasiveness evaluation. Advanced MRI, radiomics and artificial intelligence provide significant potential for tumor characterization and outcome prediction. Clinical relevance Advances in visual MRI are improving routine imaging protocols and radiomics and artificial intelligence show promise in enhancing treatment decisions through precise tumor characterization and outcome prediction.
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Affiliation(s)
- Xiaoling Gong
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zheng Ye
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yu Shen
- Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, Sanya People's Hospital, Sanya, China.
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van der Reijd DJ, Ou X, Dijkhoff RA, Drago SG, Tissier R, van Griethuysen JJ, Lambregts DM, Bakers FC, Houwers JB, Beets-Tan RG, Maas M. Selection of rectal cancer patients for organ preservation after neoadjuvant therapy: value of T2W-MRI signal intensity. Acta Radiol 2025; 66:146-154. [PMID: 39915981 DOI: 10.1177/02841851241309008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2025]
Abstract
BackgroundOrgan preservation strategies have been widely implemented for rectal cancer (RC) patients with a good response after neoadjuvant chemoradiation (nCRT). However, to accurately select eligible patients remains one of the key diagnostic challenges.PurposeTo identify eligible candidates for organ preservation after nCRT in RC, by identifying luminal response and lymph node metastases, based on T2W-MRI signal intensities.Material and MethodsA total of 171 RC patients underwent MRI before and after nCRT. The primary tumor (pre-nCRT-MRI) and tumor remnant (post-nCRT-MRI) were manually delineated. Ten signal intensity features were extracted and delta features were calculated by subtraction. Histopathological evaluation classified patients as lymph node negative (ypN0) or positive (ypN+), and as good responders (GR) or partial/poor responders (PR). Five models were constructed based on the timing of imaging.Results42/170 (25%) patients had ypN+, and 72/152 (47%) patients were considered GR. Univariate analysis showed 13/40 signal intensity features were significantly different between luminal response groups and 4/40 between nodal response groups. In multivariate analysis, the Baseline + Restaging-model yielded the best results for both luminal and nodal response with AUCs in the test set of 0.81 (95% CI=0.67-0.95) and 0.74 (95% CI=0.59-0.90), respectively. To identify PR, the Delta-model yielded an AUC of 0.72 (95% CI=0.56-0.89) and the Delta + Restaging-model an AUC of 0.81 (95% CI=0.67-0.95), both were not able to differentiate nodal response. The models including solely baseline or restaging features were not predictive.ConclusionT2W-MRI signal intensities of the primary rectal tumor are related to the luminal and nodal response after nCRT and hold promise to identify patients eligible for organ preservation.
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Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Xinde Ou
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Rebecca Ap Dijkhoff
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Silvia G Drago
- Department of Diagnostic Radiology, Ospedale San Gerardo Monza, Monza, Italy
| | - Renaud Tissier
- Biostatistics Department, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Doenja Mj Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Frans Ch Bakers
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Janneke B Houwers
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Regina Gh Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
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Zhang H, Yi H, Qin S, Liu X, Liu G. CLIP-based multimodal endorectal ultrasound enhances prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. PLoS One 2024; 19:e0315339. [PMID: 39661640 PMCID: PMC11633952 DOI: 10.1371/journal.pone.0315339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 11/22/2024] [Indexed: 12/13/2024] Open
Abstract
BACKGROUND Forecasting the patient's response to neoadjuvant chemoradiotherapy (nCRT) is crucial for managing locally advanced rectal cancer (LARC). This study investigates whether a predictive model using image-text features extracted from endorectal ultrasound (ERUS) via Contrastive Language-Image Pretraining (CLIP) can predict tumor regression grade (TRG) before nCRT. METHODS A retrospective analysis of 577 LARC patients who received nCRT followed by surgery was conducted from January 2018 to December 2023. ERUS scans and TRG were used to assess nCRT response, categorizing patients into good (TRG 0) and poor (TRG 1-3) responders. Image and text features were extracted using the ResNet50+RBT3 (RN50) and ViT-B/16+RoBERTa-wwm (VB16) components of the Chinese-CLIP model. LightGBM was used for model construction and comparison. A subset of 100 patients from each responder group was used to compare the CLIP method with manual radiomics methods (logistic regression, support vector machines, and random forest). SHapley Additive exPlanations (SHAP) technique was used to analyze feature contributions. RESULTS The RN50 and VB16 models achieved AUROC scores of 0.928 (95% CI: 0.90-0.96) and 0.900 (95% CI: 0.86-0.93), respectively, outperforming manual radiomics methods. SHAP analysis indicated that image features dominated the RN50 model, while both image and text features were significant in the VB16 model. CONCLUSIONS The CLIP-based predictive model using ERUS image-text features and LightGBM showed potential for improving personalized treatment strategies. However, this study is limited by its retrospective design and single-center data.
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Affiliation(s)
- Hanchen Zhang
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Nuclear Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hang Yi
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Si Qin
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyin Liu
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guangjian Liu
- Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Zhou T, Guan Y, Lin X, Zhou X, Mao L, Ma Y, Fan B, Li J, Tu W, Liu S, Fan L. A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients. Eur J Radiol Open 2024; 13:100580. [PMID: 38989052 PMCID: PMC11233899 DOI: 10.1016/j.ejro.2024.100580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/31/2024] [Accepted: 06/11/2024] [Indexed: 07/12/2024] Open
Abstract
Purpose It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups. Methods Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA). Results The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility. Conclusions The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.
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Affiliation(s)
- TaoHu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
- School of Medical Imaging, Shandong Second Medical University, Weifang, Shandong 261053, China
| | - Yu Guan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - XiaoQing Lin
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China
| | - XiuXiu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Liang Mao
- Department of Medical Imaging, Affiliated Hospital of Ji Ning Medical University, Ji Ning 272000, China
| | - YanQing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, ZJ, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Jie Li
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China
| | - WenTing Tu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - ShiYuan Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
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Ma T, Wang J, Ma F, Shi J, Li Z, Cui J, Wu G, Zhao G, An Q. Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer. Heliyon 2024; 10:e38927. [PMID: 39524896 PMCID: PMC11544045 DOI: 10.1016/j.heliyon.2024.e38927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 10/01/2024] [Accepted: 10/02/2024] [Indexed: 11/16/2024] Open
Abstract
Background Rectal cancer (RC) is one of the most common types of cancer worldwide. With the development of artificial intelligence (AI), the application of AI in preoperative evaluation and follow-up treatment of RC based on magnetic resonance imaging (MRI) has been the focus of research in this field. This review was conducted to develop comprehensive insight into the current research progress, hotspots, and future trends in AI based on MRI in RC, which remains to be studied. Methods Literature related to AI based on MRI and RC, as of November 2023, was obtained from the Web of Science Core Collection database. Visualization and bibliometric analyses of publication quantity and content were conducted to explore temporal trends, spatial distribution, collaborative networks, influential articles, keyword co-occurrence, and research directions. Results A total of 177 papers (152 original articles and 25 reviews) were identified from 24 countries/regions, 351 institutions, and 81 journals. Since 2019, the number of studies on this topic has rapidly increased. China and the United States have contributed the highest number of publications and institutions, cultivating the most intimate collaborative relationship. The highest number of articles derive from Sun Yat-sen University, while Frontiers in Oncology has published the highest number of relevant articles. Research on MRI-based AI in this field has mainly focused on preoperative diagnosis and prediction of treatment efficacy and prognosis. Conclusions This study provides an objective and comprehensive overview of the publications on MRI-based AI in RC and identifies the present research landscape, hotspots, and prospective trends in this field, which can provide valuable guidance for scholars worldwide.
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Affiliation(s)
- Tianming Ma
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jiawen Wang
- Department of Urology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Fuhai Ma
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jinxin Shi
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Zijian Li
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jian Cui
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Guoju Wu
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Gang Zhao
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Qi An
- Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
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Zheng Y, Chen X, Zhang H, Ning X, Mao Y, Zheng H, Dai G, Liu B, Zhang G, Huang D. Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study. Eur J Radiol 2024; 178:111591. [PMID: 39013271 DOI: 10.1016/j.ejrad.2024.111591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 06/06/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer. METHODS This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram. RESULTS The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram. CONCLUSION The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.
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Affiliation(s)
- Yongfei Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xu Chen
- Hangzhou Dianzi University Zhuoyue Honors College, Hangzhou, Zhejiang Province, China
| | - He Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xiaoxiang Ning
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Yichuan Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Hailan Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guojiao Dai
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Binghui Liu
- Department of Pathology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guohua Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
| | - Danjiang Huang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
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Liu Y, Dong TF, Li PJ, Chen LB, Song T. MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study. Magn Reson Imaging 2024; 110:170-175. [PMID: 38035947 DOI: 10.1016/j.mri.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023]
Abstract
PURPOSE To develop and validate a model based on MRI radiomics modals for predicting surgical high FIGO(IB3 and ≥ IIA2) and low FIGO(IB1, IB2, and IIA1) stages in patients with cervical carcinoma (CC). METHODS A total of 296 early-stage patients with CC (preoperative FIGO stages IB-IIA) confirmed by surgery and pathology were included in this retrospective study from two institutions For each patient,we extracted radiomics features from spectral attenuated inversion-recovery T2-weighted (SPAIR-T2W) and contrast-enhanced T1-weighted (CE-T1W) images.Manual segmentation was performed using the 3D Slicer software, while radiomics features were extracted, screened using the R software. A 2-stage feature extraction strategy involving univariate analysis and the Least Absolute Shrinkage Selection Operator technique was performed. A support vector machine-based model was eventually constructed. Predictive accuracy of the training and validation datasets was assessed in terms of area under the ROC curve (AUC). RESULTS A total of 1130 features were extracted from SPAIR-T2WI and CET1WI images respectively, in which 8 and 7 features significantly were associated with FIGO staging. AUCs of the SPAIR-T2W and CE-T1W models were were 0.803 and 0.790, respectively, in the internal validation group. In the external validation group, the AUCs were 0.767 and 0.749, respectively, which increased to 0.771 in the combined model. CONCLUSION Our study demonstrated the feasibility of radiomics features from SPAIR-T2W and CE-T1W images for the prediction of surgical FIGO stage in CC. Our proposed model thereby carries the potential as a non-invasive tool for the staging and treatment planning of this disease. ADVANCES IN KNOWLEDGE A radiomics model provide a non-invasive and objective method for the detection of FIGO staging in patients with cervical cancer before surgery, thus providing a reference for the selection of treatment options for patients.
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Affiliation(s)
- Yi Liu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, No.63, Duobao Road, Guangzhou, Guangdong 510105, China
| | - Tian-Fa Dong
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, No.63, Duobao Road, Guangzhou, Guangdong 510105, China
| | - Pei-Jun Li
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong 529000, China
| | - Liu-Bing Chen
- Department of Radiology, Guangzhou Red Cross Hospital,Guangzhou, Guangdong 510240, China
| | - Ting Song
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, No.63, Duobao Road, Guangzhou, Guangdong 510105, China.
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Singh S, Mohajer B, Wells SA, Garg T, Hanneman K, Takahashi T, AlDandan O, McBee MP, Jawahar A. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research. Acad Radiol 2024; 31:2281-2291. [PMID: 38286723 DOI: 10.1016/j.acra.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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Affiliation(s)
- Shiva Singh
- Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland
| | - Bahram Mohajer
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Shane A Wells
- Radiology, University of Michigan, Ann Arbor, Michigan
| | - Tushar Garg
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Kate Hanneman
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Omran AlDandan
- Department of Radiology, Imam Abdulrahman Bin Faisal University, College of Medicine: Dammam, Eastern, Saudi Arabia
| | - Morgan P McBee
- Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Anugayathri Jawahar
- Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611.
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10
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Wang H, Sang L, Xu J, Huang C, Huang Z. Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer. Heliyon 2024; 10:e29875. [PMID: 38720718 PMCID: PMC11076642 DOI: 10.1016/j.heliyon.2024.e29875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Objective To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC). Methods Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model's predictive efficacy was made using AUC. Results On the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884). Conclusion The nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.
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Affiliation(s)
- Haili Wang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Li Sang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co.Ltd, Beijing, China
| | - Zhaoqin Huang
- Department of Radiology, Shandong Provincial Hospital Affliated to Shandong First Medical University, Jinan, 250021, Shandong, China
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11
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Zhong X, Zeng G, Zhang L, You S, Fu Y, He W, Liao G. Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. Front Oncol 2024; 14:1361300. [PMID: 38529385 PMCID: PMC10961458 DOI: 10.3389/fonc.2024.1361300] [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: 01/30/2024] [Accepted: 02/19/2024] [Indexed: 03/27/2024] Open
Abstract
Purpose To investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT). Methods and materials For this retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated. Results Among 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm3 was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level. Conclusion Tumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.
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Affiliation(s)
- Xiaoling Zhong
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Guohua Zeng
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Lixiang Zhang
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Shuyuan You
- Department of Pathology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Yuxiang Fu
- Department of Gastrointestinal surgery, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Wan He
- Department of Oncology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
| | - Guixiang Liao
- Department of Radiation Oncology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China
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12
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Marinkovic M, Stojanovic-Rundic S, Stanojevic A, Tomasevic A, Jankovic R, Zoidakis J, Castellví-Bel S, Fijneman RJA, Cavic M, Radulovic M. Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy Prediction. J Clin Med 2024; 13:421. [PMID: 38256556 PMCID: PMC10816962 DOI: 10.3390/jcm13020421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 01/24/2024] Open
Abstract
(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma.
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Affiliation(s)
- Mladen Marinkovic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Suzana Stojanovic-Rundic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Aleksandra Stanojevic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Aleksandar Tomasevic
- Clinic for Radiation Oncology and Diagnostics, Department of Radiation Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (M.M.); (S.S.-R.); (A.T.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Radmila Jankovic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Jerome Zoidakis
- Department of Biotechnology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece;
- Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Sergi Castellví-Bel
- Gastroenterology Deparment, Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Clínic Barcelona, University of Barcelona, 08036 Barcelona, Spain;
| | - Remond J. A. Fijneman
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
| | - Milena Cavic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia; (A.S.); (R.J.); (M.C.)
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Nie T, Yuan Z, He Y, Xu H, Guo X, Liu Y. Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms. Technol Cancer Res Treat 2024; 23:15330338241305463. [PMID: 39668711 PMCID: PMC11638987 DOI: 10.1177/15330338241305463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 10/25/2024] [Accepted: 11/11/2024] [Indexed: 12/14/2024] Open
Abstract
INTRODUCTION Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate the utility of multi-parametric MRI radiomics models and identify the most accurate machine learning (ML) algorithms for predicting pT stage of RC. METHOD This retrospective study analyzed pretreatment clinical features of 171 RC patients who underwent 3 T MRI prior to neoadjuvant therapy and subsequent total mesorectal excision. Tumors were manually drawn as regions of interest (ROI) layer by layer on high-resolution T2-weighted image (T2WI) and contrast-enhanced T1-weighted image (CE-T1WI) using ITK-SNAP software. The most relevant features of pT stage from CE-T1WI, T2WI, and fusion features (combination of clinical features, CE-T1WI, and T2WI radiomics features) were extracted by the Least Absolute Shrinkage and Selection Operator method. Clinical, CE-T1WI radiomics, T2WI radiomics, and fusion models were established by ML multiple classifiers. RESULTS In the clinical model, the LightGBM algorithm demonstrated the highest efficiency, with AUC values of 0.857 and 0.702 for the training and test cohorts, respectively. For the T2WI and CE-T1WI models, the SVM algorithm was the most efficient; AUC = 0.969 and 0.868 in the training cohort, and 0.839 and 0.760 in the test cohort, respectively. The fusion model yielded the highest predictive performance using the LR algorithm; AUC = 0.967 and 0.932 in the training and test cohorts, respectively. CONCLUSION Radiomics features extracted from CE-T1WI and T2WI images and clinical features were effective predictors of pT stage in patients with rectal cancer who underwent neoadjuvant therapy. ML-based multi-parameter MRI radiomics model incorporating relevant clinical features can improve the pT stage prediction accuracy of RC.
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Affiliation(s)
- Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaofang Guo
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Sang L, Liu Z, Huang C, Xu J, Wang H. Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer. Clin Radiol 2024; 79:60-66. [PMID: 37838543 DOI: 10.1016/j.crad.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/28/2023] [Accepted: 09/12/2023] [Indexed: 10/16/2023]
Abstract
AIM To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer. MATERIALS AND METHODS Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively). CONCLUSION A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.
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Affiliation(s)
- L Sang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - Z Liu
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China
| | - C Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - J Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of, PHD Technology Co. Ltd, Beijing, China
| | - H Wang
- Department of Radiology, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, No. 324, Jingwu Road, Huaiyin District, Jinan 250012, Shandong, China.
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15
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 PMCID: PMC11301614 DOI: 10.1007/s12029-022-00909-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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Chen H, Li X, Pan X, Qiang Y, Qi XS. Feature selection based on unsupervised clustering evaluation for predicting neoadjuvant chemoradiation response for patients with locally advanced rectal cancer. Phys Med Biol 2023; 68:235012. [PMID: 37972413 DOI: 10.1088/1361-6560/ad0d46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/16/2023] [Indexed: 11/19/2023]
Abstract
Accurate response prediction allows for personalized cancer treatment of locally advanced rectal cancer (LARC) with neoadjuvant chemoradiation. In this work, we designed a convolutional neural network (CNN) feature extractor with switchable 3D and 2D convolutional kernels to extract deep learning features for response prediction. Compared with radiomics features, convolutional kernels may adaptively extract local or global image features from multi-modal MR sequences without the need of feature predefinition. We then developed an unsupervised clustering based evaluation method to improve the feature selection operation in the feature space formed by the combination of CNN features and radiomics features. While normal process of feature selection generally includes the operations of classifier training and classification execution, the process needs to be repeated many times after new feature combinations were found to evaluate the model performance, which incurs a significant time cost. To address this issue, we proposed a cost effective process to use a constructed unsupervised clustering analysis indicator to replace the classifier training process by indirectly evaluating the quality of new found feature combinations in feature selection process. We evaluated the proposed method using 43 LARC patients underwent neoadjuvant chemoradiation. Our prediction model achieved accuracy, area-under-curve (AUC), sensitivity and specificity of 0.852, 0.871, 0.868, and 0.735 respectively. Compared with traditional radiomics methods, the prediction models (AUC = 0.846) based on deep learning-based feature sets are significantly better than traditional radiomics methods (AUC = 0.714). The experiments also showed following findings: (1) the features with higher predictive power are mainly from high-order abstract features extracted by CNN on ADC images and T2 images; (2) both ADC_Radiomics and ADC_CNN features are more advantageous for predicting treatment responses than the radiomics and CNN features extracted from T2 images; (3) 3D CNN features are more effective than 2D CNN features in the treatment response prediction. The proposed unsupervised clustering indicator is feasible with low computational cost, which facilitates the discovery of valuable solutions by highlighting the correlation and complementarity between different types of features.
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Affiliation(s)
- Hao Chen
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, xi'an 710121, People's Republic of China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, People's Republic of China
| | - Xing Li
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, xi'an 710121, People's Republic of China
| | - Xiaoying Pan
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, xi'an 710121, People's Republic of China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, People's Republic of China
| | - Yongqian Qiang
- First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - X Sharon Qi
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, United States of America
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Huang H, Han L, Guo J, Zhang Y, Lin S, Chen S, Lin X, Cheng C, Guo Z, Qiu Y. Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer. Radiat Oncol 2023; 18:179. [PMID: 37907928 PMCID: PMC10619290 DOI: 10.1186/s13014-023-02368-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI). METHODS In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87). CONCLUSIONS Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.
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Affiliation(s)
- Hongyan Huang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Lujun Han
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Jianbo Guo
- Department of Radiology, Meizhou People's Hospital, No. 63 Huangtang Road, Meizhou, 514000, China
| | - Yanyu Zhang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China
| | - Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Xiaoshan Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Caixue Cheng
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China
| | - Zheng Guo
- Department of Hematology and Oncology, International Cancer Center, Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University Health Science Center, Xueyuan AVE 1098, Nanshan District, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Yingwei Qiu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China.
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China.
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Zhou X, Yu Y, Feng Y, Ding G, Liu P, Liu L, Ren W, Zhu Y, Cao W. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Radiat Oncol 2023; 18:175. [PMID: 37891611 PMCID: PMC10612200 DOI: 10.1186/s13014-023-02352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/13/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC. METHODS This retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital. RESULTS The deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism. CONCLUSIONS The self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.
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Affiliation(s)
- Xuezhi Zhou
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yi Yu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yanru Feng
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Guojun Ding
- Department of Radiology, Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Luying Liu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China
| | - Wenjie Ren
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, Henan, China.
| | - Yuan Zhu
- Department of Radiation Oncology, Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, China.
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Jiang H, Guo W, Yu Z, Lin X, Zhang M, Jiang H, Zhang H, Sun Z, Li J, Yu Y, Zhao S, Hu H. A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer. Acad Radiol 2023; 30 Suppl 1:S185-S198. [PMID: 37394412 DOI: 10.1016/j.acra.2023.04.032] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/18/2023] [Accepted: 04/23/2023] [Indexed: 07/04/2023]
Abstract
RATIONALE AND OBJECTIVES To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters. MATERIALS AND METHODS Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed. RESULTS The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model. CONCLUSION Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.
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Affiliation(s)
- Hao Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Wei Guo
- Department of PET/CT-MRI, Harbin Medical University Cancer Hospital, Harbin, China (W.G.)
| | - Zhuo Yu
- Huiying Medical Technology (Beijing) Co, Beijing, China (Z.Y.)
| | - Xue Lin
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Mingyu Zhang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Affiliated to Capital Medical University, Beijing, China (M.Z.)
| | - Huijie Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.).
| | - Hongxia Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (H.Z., Y.Y.)
| | - Zhongqi Sun
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Jinping Li
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Yanyan Yu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (H.Z., Y.Y.)
| | - Sheng Zhao
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
| | - Hongbo Hu
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (H.J., X.L., H.J., Z.S., J.L., S.Z., H.H.)
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20
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Kim HR, Kim SH, Nam KH. Association between Dynamic Contrast-Enhanced MRI Parameters and Prognostic Factors in Patients with Primary Rectal Cancer. Curr Oncol 2023; 30:2543-2554. [PMID: 36826155 PMCID: PMC9955503 DOI: 10.3390/curroncol30020194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND To evaluate the association between perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with prognostic factors in primary rectal cancer patients. METHODS A sample of 51 patients with pathologically proven rectal adenocarcinoma through surgery were retrospectively enrolled. All the patients underwent preoperative DCE-MRI including 3D-spoiled gradient echo. Two radiologists determined the tumor border after radiologic-pathologic correlation and drew regions of interest. The perfusion parameters, including the volume transfer constant (Ktrans), were calculated under the extended Toft model. The prognostic factors included TN stage, circumferential resection margin, extramural venous invasion, Kirsten-ras mutation, tumor size, carcinoembryonic antigen, and tumor differentiation. The association was assessed via correlation or t-test. For significant prognostic factors, receiver operating characteristic (ROC) curve analyses were performed to estimate the diagnostic predictive values. RESULTS Ktrans only showed a significant difference according to tumor differentiation, between the well-differentiated (n = 6) and moderately differentiated (n = 45) groups (0.127 ± 0.032, 0.084 ± 0.036, p = 0.036). The AUC was 0.838 (95% CI, 0.702-0.929), and the estimated accuracy, sensitivity, and specificity were 87%, 90%, and 60%, respectively. CONCLUSIONS Ktrans showed a significant difference based on tumor differentiation, which may be conducive to prediction of prognosis in primary rectal cancer.
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Affiliation(s)
- Hye Ri Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
- Correspondence: ; Tel.: +82-51-797-0382
| | - Kyung Han Nam
- Department of Pathology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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22
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Wang H, Chen X, Ding J, Deng S, Mao G, Tian S, Zhu X, Ao W. Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer. Abdom Radiol (NY) 2023; 48:471-485. [PMID: 36508131 DOI: 10.1007/s00261-022-03759-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. METHODS This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance. RESULTS The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice. CONCLUSION A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.
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Affiliation(s)
- Hui Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Xiaoyong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jingfeng Ding
- Department of Radiology, Shanghai Putuo District People's Hospital, Shanghai, China
| | - Shuitang Deng
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Shuyuan Tian
- Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiandi Zhu
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Ouyang G, Chen Z, Dou M, Luo X, Wen H, Deng X, Meng W, Yu Y, Wu B, Jiang D, Wang Z, Yao Y, Wang X. Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data. Technol Cancer Res Treat 2023; 22:15330338231186467. [PMID: 37431270 PMCID: PMC10338728 DOI: 10.1177/15330338231186467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 07/12/2023] Open
Abstract
PURPOSE To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods. METHODS Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models. RESULTS Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2. CONCLUSION There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.
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Affiliation(s)
- Ganlu Ouyang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhebin Chen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Meng Dou
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xu Luo
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Han Wen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiangbing Deng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjian Meng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yongyang Yu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bing Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Yao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models. J Gastrointest Surg 2023; 27:122-130. [PMID: 36271199 DOI: 10.1007/s11605-022-05477-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/25/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear. METHODS A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves. RESULTS Of 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73). CONCLUSIONS The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.
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Ma Q, Yi Y, Liu T, Wen X, Shan F, Feng F, Yan Q, Shen J, Yang G, Shi Y. MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter study. Eur Radiol 2022; 32:8659-8669. [PMID: 35748898 PMCID: PMC9226270 DOI: 10.1007/s00330-022-08911-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 04/27/2022] [Accepted: 05/27/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVE To develop and evaluate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for identification of invisible basal cisterns changes in tuberculous meningitis (TBM) patients. METHODS Our retrospective study enrolled 184 TBM patients and 187 non-TBM controls from 3 Chinese hospitals (training dataset, 158 TBM patients and 159 non-TBM controls; testing dataset, 26 TBM patients and 28 non-TBM controls). nnU-Net was used to segment basal cisterns in fluid-attenuated inversion recovery (FLAIR) images. Subsequently, radiomics features were extracted from segmented basal cisterns in FLAIR and T2-weighted (T2W) images. Feature selection was carried out in three steps. Support vector machine (SVM) and logistic regression (LR) classifiers were applied to construct the radiomics signature to directly identify basal cisterns changes in TBM patients. Finally, the diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). RESULTS The segmentation model achieved the mean Dice coefficients of 0.920 and 0.727 in the training and testing datasets, respectively. The SVM model with 7 T2WI-based radiomics features achieved best discrimination capability for basal cisterns changes with an AUC of 0.796 (95% CI, 0.744-0.847) in the training dataset, and an AUC of 0.751 (95% CI, 0.617-0.886) with good calibration in the testing dataset. DCA confirmed its clinical usefulness. CONCLUSION The T2WI-based radiomics signature combined with deep learning segmentation could provide a fully automatic, non-invasive tool to identify invisible changes of basal cisterns, which has the potential to assist in the diagnosis of TBM. KEY POINTS • The T2WI-based radiomics signature was useful for identifying invisible basal cistern changes in TBM. • The nnU-Net model achieved acceptable results for the auto-segmentation of basal cisterns. • Combining radiomics and deep learning segmentation provided an automatic, non-invasive approach to assist in the diagnosis of TBM.
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Affiliation(s)
- Qiong Ma
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
| | - Yinqiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Tiejun Liu
- Department of Radiology, Liuzhou People's Hospital, Liuzhou, Guangxi Zhuang Autonomous Region, China
| | - Xinnian Wen
- Department of Radiology, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi Zhuang Autonomous Region, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Feng Feng
- Department of Radiology, Nantong Tumor Hospital, Nantong, Jiangsu, China
| | - Qinqin Yan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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Zhang J, Cui Y, Wei K, Li Z, Li D, Song R, Ren J, Gao X, Yang X. Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study. Gastric Cancer 2022; 25:1050-1059. [PMID: 35932353 DOI: 10.1007/s10120-022-01328-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/21/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients. METHODS A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC). RESULTS The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05). CONCLUSIONS A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.
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Affiliation(s)
- Jiayi Zhang
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, Guangdong, China
| | - Kaikai Wei
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, Yunnan, China
| | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
| | - Ruirui Song
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
| | | | - Xin Gao
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
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Eresen A, Sun C, Zhou K, Shangguan J, Wang B, Pan L, Hu S, Ma Q, Yang J, Zhang Z, Yaghmai V. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI. Acad Radiol 2022; 29:1378-1386. [PMID: 34933803 PMCID: PMC10029937 DOI: 10.1016/j.acra.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.
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Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Quanhong Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Jia Yang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California.
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30
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Zhang Z, Yi X, Pei Q, Fu Y, Li B, Liu H, Han Z, Chen C, Pang P, Lin H, Gong G, Yin H, Zai H, Chen BT. CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer. Cancer Med 2022; 12:2463-2473. [PMID: 35912919 PMCID: PMC9939108 DOI: 10.1002/cam4.5086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/05/2022] [Accepted: 05/07/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.
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Affiliation(s)
- Zinan Zhang
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,Department of Gastroenterology (The Third Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Xiaoping Yi
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,National Engineering Research Center of Personalized Diagnostic and Therapeutic TechnologyXiangya HospitalChangshaHunanP.R. China,National Clinical Research Center for Geriatric Disorders (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,Hunan Key Laboratory of Skin Cancer and PsoriasisChangshaHunanP.R. China,Hunan Engineering Research Center of Skin Health and DiseaseChangshaHunanP.R. China
| | - Qian Pei
- Department of General Surgery (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Yan Fu
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China,National Engineering Research Center of Personalized Diagnostic and Therapeutic TechnologyXiangya HospitalChangshaHunanP.R. China
| | - Bin Li
- Department of Oncology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Haipeng Liu
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Zaide Han
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Changyong Chen
- Department of Radiology (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Peipei Pang
- Department of Pharmaceuticals and DiagnosisGE HealthcareChangshaP.R. China
| | - Huashan Lin
- Department of Pharmaceuticals and DiagnosisGE HealthcareChangshaP.R. China
| | - Guanghui Gong
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunanP.R. China
| | - Hongling Yin
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaHunanP.R. China
| | - Hongyan Zai
- Department of General Surgery (Xiangya Hospital)Central South UniversityChangshaHunanP.R. China
| | - Bihong T. Chen
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCaliforniaUSA
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31
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Mbanu P, Saunders MP, Mistry H, Mercer J, Malcomson L, Yousif S, Price G, Kochhar R, Renehan AG, van Herk M, Osorio EV. Clinical and radiomics prediction of complete response in rectal cancer pre-chemoradiotherapy. Phys Imaging Radiat Oncol 2022; 23:48-53. [PMID: 35800297 PMCID: PMC9253904 DOI: 10.1016/j.phro.2022.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 06/18/2022] [Accepted: 06/20/2022] [Indexed: 11/11/2022] Open
Abstract
Background and purpose Patients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables' ability to predict cCR in patients pre chemoradiotherapy. Materials and methods Using the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced. Results Using Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69-0.83) in the training set and 0.68 (95% CI 0.57-0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66-0.80) and 0.62 (95% CI 0.51-0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61-0.75) and 0.66 (95% CI 0.56-0.77) in the training and validation sets. Conclusion The predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.
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Affiliation(s)
- Peter Mbanu
- Department of Clinical Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Mark P. Saunders
- Department of Clinical Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Hitesh Mistry
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Joe Mercer
- Department of Radiological Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Lee Malcomson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Colorectal and Peritoneal Oncology Centre, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Saif Yousif
- Department of Clinical Oncology, Lancashire Teaching Hospital, Preston, United Kingdom
| | - Gareth Price
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Rohit Kochhar
- Department of Radiological Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Andrew G. Renehan
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Colorectal and Peritoneal Oncology Centre, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Marcel van Herk
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
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Nie K, Hu P, Zheng J, Zhang Y, Yang P, Jabbour SK, Yue N, Dong X, Xu S, Shen B, Niu T, Hu X, Cai X, Sun J. Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer. Front Oncol 2022; 12:779030. [PMID: 35847948 PMCID: PMC9279662 DOI: 10.3389/fonc.2022.779030] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II-III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.
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Affiliation(s)
- Ke Nie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Peng Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianjun Zheng
- Department of Radiology, Hwa Mei Hospital, Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, University of Chinese Academy of Sciences, Ningbo, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Pengfei Yang
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Salma K. Jabbour
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Ning Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Xue Dong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shufeng Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tianye Niu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotong Hu
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiujun Cai
- Department of General Surgery, Innovation Center for Minimally Invasive Techniques and Devices, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Innovation Center for Minimally Invasive Techniques and Devices, Zhejiang University, Hangzhou, China
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Shahzadi I, Zwanenburg A, Lattermann A, Linge A, Baldus C, Peeken JC, Combs SE, Diefenhardt M, Rödel C, Kirste S, Grosu AL, Baumann M, Krause M, Troost EGC, Löck S. Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Sci Rep 2022; 12:10192. [PMID: 35715462 PMCID: PMC9205935 DOI: 10.1038/s41598-022-13967-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/17/2022] [Indexed: 11/21/2022] Open
Abstract
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.
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Affiliation(s)
- Iram Shahzadi
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alex Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Annika Lattermann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Annett Linge
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Baldus
- Department of Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jan C Peeken
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Stephanie E Combs
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Markus Diefenhardt
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Claus Rödel
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anca-Ligia Grosu
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Baumann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mechthild Krause
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Esther G C Troost
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Steffen Löck
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. .,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
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Zhang X, Wang Y, Zhang J, Zhang L, Wang S, Chen Y. Development of a MRI-Based Radiomics Nomogram for Prediction of Response of Patients With Muscle-Invasive Bladder Cancer to Neoadjuvant Chemotherapy. Front Oncol 2022; 12:878499. [PMID: 35646654 PMCID: PMC9132152 DOI: 10.3389/fonc.2022.878499] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/14/2022] [Indexed: 12/12/2022] Open
Abstract
Objective To develop and evaluate the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram for prediction of response of patients with muscle-invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC). Methods A total of 70 patients with clinical T2-4aN0M0 MIBC were enrolled in this retrospective study. For each patient, 1316 radiomics features were extracted from T2-weighted images (T2WI), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. The variance threshold algorithm and the Student's t-test or the Mann-Whitney U test were applied to select optimal features. Multivariate logistic regression analysis was used to eliminate irrelevant features, and the retained features were incorporated into the final single-modality radiomics model. Combined radiomic models were generated by combining single-modality radiomics models. A radiomics nomogram, incorporating radiomics signatures and independent clinical risk factors, was developed to determine whether the performance of the model in predicting tumor response to NAC could be further improved. Results Based on pathological T stage post-surgery, 36 (51%) patients were classified as good responders (GR) and 34 (49%) patients as non-good responders (non-GR). In addition, 3 single-modality radiomics models and 4 combined radiomics models were established. Among all radiomics models, the combined radiomics model based on T2WI_Score, DWI_Score, and ADC_Score yielded the highest area under the receiver operating characteristics curve (AUC) (0.967, 95% confidence interval (CI): 0.930-0.995). A radiomics nomogram, integrating the clinical T stage and 3 single-modality radiomics models, yielded a higher AUC (0.973, 95%CI: 0.934-0.998) than other combined radiomics models. Conclusion The proposed MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for the quantitatively prediction of tumor response to NAC in patients with MIBC.
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Affiliation(s)
- Xinxin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yichen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lianyu Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Tissue-Based Markers as a Tool to Assess Response to Neoadjuvant Radiotherapy in Rectal Cancer-Systematic Review. Int J Mol Sci 2022; 23:ijms23116040. [PMID: 35682714 PMCID: PMC9181431 DOI: 10.3390/ijms23116040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 02/01/2023] Open
Abstract
According to current guidelines, the current treatment for locally advanced rectal cancer is neoadjuvant therapy, followed by a total mesorectal excision. However, radiosensitivity tends to differ among patients due to tumor heterogeneity, making it difficult to predict the possible outcomes of the neoadjuvant therapy. This review aims to investigate different types of tissue-based biomarkers and their capability of predicting tumor response to neoadjuvant therapy in patients with locally advanced rectal cancer. We identified 169 abstracts in NCBI PubMed, selected 48 reports considered to meet inclusion criteria and performed this systematic review. Multiple classes of molecular biomarkers, such as proteins, DNA, micro-RNA or tumor immune microenvironment, were studied as potential predictors for rectal cancer response; nonetheless, no literature to date has provided enough sufficient evidence for any of them to be introduced into clinical practice.
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MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer. Br J Cancer 2022; 127:249-257. [PMID: 35368044 PMCID: PMC9296479 DOI: 10.1038/s41416-022-01786-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 01/29/2022] [Accepted: 03/08/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).
Methods
Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.
Results
We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.
Conclusions
The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.
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Chen Y, Li B, Jiang Z, Li H, Dang Y, Tang C, Xia Y, Zhang H, Song B, Long L. Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer. Abdom Radiol (NY) 2022; 47:1276-1290. [PMID: 35166938 DOI: 10.1007/s00261-021-03397-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. METHODS This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. RESULTS 110 participants with the age of 60.7 ± 12.5 years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000 s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000 s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). CONCLUSION Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.
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Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zijian Jiang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Hui Li
- Department of Anus and Intestine Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Cheng Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, 100192, China
| | | | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Gaungxi Medical University, Nanning, 530021, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Tang B, Lenkowicz J, Peng Q, Boldrini L, Hou Q, Dinapoli N, Valentini V, Diao P, Yin G, Orlandini LC. Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. BMC Med Imaging 2022; 22:44. [PMID: 35287607 PMCID: PMC8919611 DOI: 10.1186/s12880-022-00773-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/04/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo‑radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice. METHODS A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics. RESULTS The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model. CONCLUSIONS The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.
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Affiliation(s)
- Bin Tang
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China.,Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Jacopo Lenkowicz
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Qian Peng
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China.
| | - Luca Boldrini
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China.
| | - Nicola Dinapoli
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Dipartimento Scienze Radiologiche, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Peng Diao
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Chengdu, China
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Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, Zhang L. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur J Radiol 2022; 151:110243. [DOI: 10.1016/j.ejrad.2022.110243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/22/2022] [Accepted: 03/05/2022] [Indexed: 12/23/2022]
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External Validation of a Radiomics Model for the Prediction of Complete Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer. Cancers (Basel) 2022; 14:cancers14041079. [PMID: 35205826 PMCID: PMC8870201 DOI: 10.3390/cancers14041079] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/10/2022] [Accepted: 02/13/2022] [Indexed: 12/29/2022] Open
Abstract
Objective: Our objective was to develop a radiomics model based on magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CE-CT) to predict pathological complete response (pCR) to neoadjuvant treatment in locally advanced rectal cancer (LARC). Material: All patients treated for a LARC with neoadjuvant CRT and subsequent surgery in two separate institutions between 2012 and 2019 were considered. Both pre-CRT pelvic MRI and CE-CT were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial MRI sequences and on CE-CT. In total, 88 radiomic parameters were extracted from each sequence using the Miras© software, with a total of 822 features by patient. The cohort was split into training (Institution 1) and testing (Institution 2) sets. The ComBat and Synthetic Minority Over-sampling Technique (SMOTE) approaches were used to account for inter-institution heterogeneity and imbalanced data, respectively. We selected the most predictive characteristics using Spearman's rank correlation and the Area Under the ROC Curve (AUC). Five pCR prediction models (clinical, radiomics before and after ComBat, and combined before and after ComBat) were then developed on the training set with a neural network approach and a bootstrap internal validation (n = 1000 replications). A cut-off maximizing the model's performance was defined on the training set. Each model was then evaluated on the testing set using sensitivity, specificity, balanced accuracy (Bacc) with the predefined cut-off. Results: Out of the 124 included patients, 14 had pCR (11.3%). After ComBat harmonization, the radiomic and the combined models obtained a Bacc of 68.2% and 85.5%, respectively, while the clinical model and the pre-ComBat combined achieved respective Baccs of 60.0% and 75.5%. Conclusions: After correction of inter-site variability and imbalanced data, addition of radiomic features enhances the prediction of pCR after neoadjuvant CRT in LARC.
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Shin J, Seo N, Baek SE, Son NH, Lim JS, Kim NK, Koom WS, Kim S. MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy. Radiology 2022; 303:351-358. [PMID: 35133200 DOI: 10.1148/radiol.211986] [Citation(s) in RCA: 117] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Preoperative assessment of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT) is increasingly needed for organ preservation, but large-scale validation of an MRI radiomics model remains lacking. Purpose To evaluate radiomics models based on T2-weighted imaging and diffusion-weighted MRI for predicting pCR after nCRT in LARC and compare their performance with visual assessment by radiologists. Materials and Methods This retrospective study included patients with LARC (clinical stage T3 or higher, positive nodal status, or both) who underwent post-nCRT MRI and elective resection between January 2009 and December 2018. Surgical histopathologic analysis was the reference standard for pCR. Radiomic features were extracted from the volume of interest on T2-weighted images and apparent diffusion coefficient (ADC) maps from post-nCRT MRI to generate three models: T2 weighted, ADC, and both T2 weighted and ADC (merged). Radiomics signatures were generated using the least absolute shrinkage and selection operator with tenfold cross-validation. Three experienced radiologists independently rated tumor regression grades at MRI and compared these with the radiomics models' diagnostic outcomes. Areas under the curve (AUCs) of the radiomics models and pooled readers were compared by using the DeLong method. Results Among 898 patients, 189 (21%) achieved pCR. The patients were chronologically divided into training (n = 592; mean age ± standard deviation, 59 years ± 12; 388 men) and test (n = 306; mean age, 59 years ± 12; 190 men) sets. The radiomics signatures of the T2-weighted, ADC, and merged models demonstrated AUCs of 0.82, 0.79, and 0.82, respectively, with no evidence of a difference found between the T2-weighted and merged models (P = .49), while the ADC model performed worse than the merged model (P = .02). The T2-weighted model had higher classification performance (AUC, 0.82 vs 0.74 [P = .009]) and sensitivity (80.0% vs 15.6% [P < .001]), but lower specificity (68.4% vs 98.6% [P < .001]) than the pooled performance of the three radiologists. Conclusion An MRI-based radiomics model showed better classification performance than experienced radiologists for diagnosing pathologic complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Taylor in this issue.
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Affiliation(s)
- Jaeseung Shin
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Nieun Seo
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Song-Ee Baek
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Nak-Hoon Son
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Joon Seok Lim
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Nam Kyu Kim
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Woong Sub Koom
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
| | - Sungwon Kim
- From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea
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Miranda J, Tan GXV, Fernandes MC, Yildirim O, Sims JA, de Arimateia Batista Araujo-Filho J, Machado FADM, Assuncao AN, Nomura CH, Horvat N. Rectal MRI radiomics for predicting pathological complete response: Where we are. Clin Imaging 2022; 82:141-149. [PMID: 34826772 PMCID: PMC9119743 DOI: 10.1016/j.clinimag.2021.10.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/21/2021] [Accepted: 10/11/2021] [Indexed: 02/03/2023]
Abstract
Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Diagnosticos da America SA (DASA), Sao Paulo, SP, Brazil
| | - Gary Xia Vern Tan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John A. Sims
- Department of Biomedical Engineering, Universidade Federal do ABC, Santo Andre, SP, Brazil
| | | | | | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Hospital Sirio-Libanes, Sao Paulo, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Zhang J, Wang G, Ren J, Yang Z, Li D, Cui Y, Yang X. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma. Eur Radiol 2022; 32:4079-4089. [DOI: 10.1007/s00330-021-08504-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/01/2021] [Accepted: 12/04/2021] [Indexed: 12/22/2022]
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Zhao W, Huang X, Wang G, Guo J. PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma. Cancer Imaging 2022; 22:7. [PMID: 35022071 PMCID: PMC8756708 DOI: 10.1186/s40644-021-00438-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/05/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). METHODS The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. RESULTS By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413-0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356-0.9999)). CONCLUSIONS For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.
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Affiliation(s)
- Wenzhe Zhao
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Xin Huang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Geliang Wang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China.
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A nomogram for predicting good response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a retrospective, double-center, cohort study. Int J Colorectal Dis 2022; 37:2157-2166. [PMID: 36048198 PMCID: PMC9560928 DOI: 10.1007/s00384-022-04247-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2022] [Indexed: 02/04/2023]
Abstract
AIM The purpose of this study was to explore the clinical factors associated with achieving good response after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and to develop and validate a nomogram. METHODS A total of 1724 consecutive LARC patients treated at Fujian Medical University Union Hospital from January 2010 to December 2021 were retrospectively evaluated as the training cohort; 267 consecutive LARC patients treated at Zhangzhou Affiliated Hospital of Fujian Medical University during the same period were evaluated as the external 2 cohorts. Based on the pathological results after radical surgery, treatment response was defined as follows: good response, stage ypT0∼2N0M0 and poor response, ypT3∼4N0M0 and/or N positive. Independent influencing factors were analyzed by logistic regression, a nomogram was developed and validated, and the model was evaluated using internal and external data cohorts for validation. RESULTS In the training cohort, 46.6% of patients achieved good response after nCRT combined with radical surgery. The rate of the retained anus was higher in the good response group (93.5% vs. 90.7%, P < 0.001). Cox regression analysis showed that the risk of overall survival and disease-free survival was significantly lower among good response patients than poor response patients, HR = 0.204 (95%CI: 0.146-0.287). Multivariate logistic regression analysis showed an independent association with 9 clinical factors, including histopathology, and a nomogram with an excellent predictive response was developed accordingly. The C-index of the predictive accuracy of the nomogram was 0.764 (95%CI: 0.742-0.786), the internal validation of the 200 bootstrap replication mean C-index was 0.764, and the external validation cohort showed an accuracy C-index of 0.789 (95%CI: 0.734-0.844), with good accuracy of the model. CONCLUSION We identified factors associated with achieving good response in LARC after treatment with nCRT and developed a nomogram to contribute to clinical decision-making.
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Zhang D, Duan Y, Guo J, Wang Y, Yang Y, Li Z, Wang K, Wu L, Yu M. Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:4300108. [PMID: 35317416 PMCID: PMC8932521 DOI: 10.1109/jtehm.2022.3156851] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 02/06/2023]
Abstract
Background: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. Methods: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. Results: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method’s generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. Conclusion: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Clinical and Translational Impact Statement—This study aims to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer to assist clinicians quickly diagnose and formulate personalized treatment plans for patients.
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Affiliation(s)
- Dehai Zhang
- School of SoftwareYunnan University Kunming 650106 China
| | - Yongchun Duan
- School of SoftwareYunnan University Kunming 650106 China
| | - Jing Guo
- School of Information Science and EngineeringYunnan University Kunming 650106 China
| | - Yaowei Wang
- School of SoftwareYunnan University Kunming 650106 China
| | - Yun Yang
- Key Laboratory in Software Engineering of Yunnan Province, School of SoftwareYunnan University Kunming 650504 China
| | - Zhenhui Li
- Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center Kunming 650118 China
| | - Kelong Wang
- School of SoftwareYunnan University Kunming 650106 China
| | - Lin Wu
- Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center Kunming 650118 China
| | - Minghao Yu
- School of SoftwareYunnan University Kunming 650106 China
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Zhang S, Yu M, Chen D, Li P, Tang B, Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol Rep 2021; 47:34. [PMID: 34935061 PMCID: PMC8717123 DOI: 10.3892/or.2021.8245] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.
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Affiliation(s)
- Siyu Zhang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Mingrong Yu
- College of Physical Education, Sichuan Agricultural University, Ya'an, Sichuan 625000, P.R. China
| | - Dan Chen
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Peidong Li
- Second Department of Gastrointestinal Surgery, The Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, P.R. China
| | - Bin Tang
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
| | - Jie Li
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
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Wada Y, Shimada M, Morine Y, Ikemoto T, Saito Y, Zhu Z, Wang X, Etxart A, Park Y, Bujanda L, Park IJ, Goel A. Circulating miRNA Signature Predicts Response to Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer. JCO Precis Oncol 2021; 5:PO.21.00015. [PMID: 34913022 PMCID: PMC8668014 DOI: 10.1200/po.21.00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 08/22/2021] [Accepted: 10/15/2021] [Indexed: 11/20/2022] Open
Abstract
UNLABELLED Patients with locally advanced rectal cancer (LARC) are recommended to receive preoperative chemoradiotherapy (PCRT) followed by surgery. Response to PCRT varies widely: 60%-70% of patients with LARC do not derive therapeutic benefit from PCRT, whereas 15%-20% of patients achieve pathologic complete response (pCR). We sought to develop a liquid biopsy assay for identifying response to PCRT in patients with LARC. MATERIALS AND METHODS We analyzed two genome-wide microRNA (miRNA) expression profiling data sets from tumor tissue samples for in silico discovery (GSE68204) and validation (GSE29298). We prioritized biomarkers in pretreatment plasma specimens from clinical training (n = 41; 15 responders and 26 nonresponders) and validation (n = 65; 29 responders and 36 nonresponders) cohorts of patients with LARC. We developed an integrated miRNA panel and established a risk assessment model, which was combined with the miRNA panel and carcinoembryonic antigen levels. RESULTS Our comprehensive discovery effort identified an 8-miRNA panel that robustly predicted response to PCRT, with an excellent accuracy in the discovery (area under the curve [AUC] = 0.95) and validation (AUC = 0.92) cohorts. We successfully established a circulating miRNA panel with remarkable diagnostic accuracy in the clinical training (AUC = 0.82) and validation (AUC = 0.81) cohorts. Moreover, the predictive accuracy of the panel was significantly superior to conventional clinical factors in both cohorts (P < .01) and the risk assessment model was superior (AUC = 0.83). Finally, we applied our model to detect patients with pathologic complete response and showed that it was dramatically superior to currently used pathologic features (AUC = 0.92). CONCLUSION Our novel risk assessment signature for predicting response to PCRT has a potential for clinical translation as a liquid biopsy assay in patients with LARC.
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Affiliation(s)
- Yuma Wada
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Monrovia, CA
- Department of Surgery, Tokushima University, Tokushima, Japan
- Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX
| | - Mitsuo Shimada
- Department of Surgery, Tokushima University, Tokushima, Japan
| | - Yuji Morine
- Department of Surgery, Tokushima University, Tokushima, Japan
| | - Tetsuya Ikemoto
- Department of Surgery, Tokushima University, Tokushima, Japan
| | - Yu Saito
- Department of Surgery, Tokushima University, Tokushima, Japan
| | - Zhongxu Zhu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Ane Etxart
- Department of Surgery, Donostia Hospital University, Instituto Biodonostia, San Sebastián, Spain
| | - Yangsoon Park
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Luis Bujanda
- Gastroenterology Department, Instituto Biodonostia, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Universidad del País Vasco (UPV/EHU), San Sebastián, Spain
| | - In Ja Park
- Division of Colon and Rectal Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ajay Goel
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Monrovia, CA
- Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX
- City of Hope Comprehensive Cancer Center, Duarte, CA
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Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, Wei W, Dai W, Tang Z, Ding Y, Cai G, Tong T, Meng X, Tian J. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine 2021; 69:103442. [PMID: 34157487 PMCID: PMC8237293 DOI: 10.1016/j.ebiom.2021.103442] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/17/2021] [Accepted: 06/01/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. METHODS We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. FINDINGS DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05). INTERPRETATION MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. FUNDING The funding bodies that contributed to this study are listed in the Acknowledgements section.
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Affiliation(s)
- Xiangyu Liu
- 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; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dafu Zhang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100080, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Peiyi Xie
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China
| | - Kai Sun
- 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; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Wei Wei
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Weixing Dai
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Zhenchao Tang
- Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
| | - Yingying Ding
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Guoxiang Cai
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Xiaochun Meng
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China.
| | - Jie Tian
- 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; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China.
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Zhuang Z, Liu Z, Li J, Wang X, Xie P, Xiong F, Hu J, Meng X, Huang M, Deng Y, Lan P, Yu H, Luo Y. Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer. J Transl Med 2021; 19:256. [PMID: 34112180 PMCID: PMC8194221 DOI: 10.1186/s12967-021-02919-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/31/2021] [Indexed: 01/06/2023] Open
Abstract
Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02919-x.
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Affiliation(s)
- Zhuokai Zhuang
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Zongchao Liu
- Department of Biostatistics, Columbia University, New York, NY, 10032, USA
| | - Juan Li
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Xiaolin Wang
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Peiyi Xie
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Fei Xiong
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Jiancong Hu
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Xiaochun Meng
- Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Meijin Huang
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Yanhong Deng
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Department of Medical Oncology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, Guangdong, China
| | - Ping Lan
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.,Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China
| | - Huichuan Yu
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.
| | - Yanxin Luo
- Department of Colorectal Surgery, Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, Guangdong, China.
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