Xuehua Li1, Dong Liang2, Jixin Meng1, Jie Zhou3, Zhao Chen4, Siyun Huang1, Baolan Lu1, Yun Qiu5, Mark E Baker6, Ziyin Ye7, Qinghua Cao7, Mingyu Wang2, Chenglang Yuan2, Zhihui Chen8, Shengyu Feng2, Yuxuan Zhang2, Marietta Iacucci9, Subrata Ghosh9, Florian Rieder10, Canhui Sun1, Minhu Chen5, Ziping Li1, Ren Mao11, Bingsheng Huang12, Shi-Ting Feng13. 1. Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. 2. Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, People's Republic of China. 3. Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. 4. Department of Medical Imaging Center, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China. 5. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. 6. Section of Abdominal Imaging, Imaging Institute, Digestive Disease Institute and Cancer Institute, Cleveland Clinic, Cleveland, Ohio. 7. Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. 8. Department of Gastrointestinal and Pancreatic Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. 9. National Institute for Health Research Biomedical Research Institute, Institute of Translational Medicine, University of Birmingham, University Hospitals Birmingham National Health Service Foundation Trust, United Kingdom. 10. Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic, Cleveland, Ohio. 11. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic, Cleveland, Ohio. Electronic address: maor5@mail.sysu.edu.cn. 12. Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, People's Republic of China. Electronic address: huangb@szu.edu.cn. 13. Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. Electronic address: fengsht@mail.sysu.edu.cn.
Abstract
BACKGROUND & AIMS: No reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD. METHODS: This retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe. In the training cohort, 1454 radiomic features were extracted from venous-phase CTE and a machine learning-based RM was developed based on the reproducible features using logistic regression. The RM was validated in an independent external test cohort recruited from 3 centers. The diagnostic performance of RM was compared with 2 radiologists' visual interpretation of CTE using receiver operating characteristic (ROC) curve analysis. RESULTS: In the training cohort, the area under the ROC curve (AUC) of RM for distinguishing moderate-severe from none-mild intestinal fibrosis was 0.888 (95% confidence interval [CI], 0.818-0.957). In the test cohort, the RM showed robust performance across 3 centers with an AUC of 0.816 (95% CI, 0.706-0.926), 0.724 (95% CI, 0.526-0.923), and 0.750 (95% CI, 0.560-0.940), respectively. Moreover, the RM was more accurate than visual interpretations by either radiologist (radiologist 1, AUC = 0.554; radiologist 2, AUC = 0.598; both, P < .001) in the test cohort. Decision curve analysis showed that the RM provided a better net benefit to predicting intestinal fibrosis than the radiologists. CONCLUSIONS: A CTE-based RM allows for accurate characterization of intestinal fibrosis in CD.
BACKGROUND & AIMS: No reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD. METHODS: This retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe. In the training cohort, 1454 radiomic features were extracted from venous-phase CTE and a machine learning-based RM was developed based on the reproducible features using logistic regression. The RM was validated in an independent external test cohort recruited from 3 centers. The diagnostic performance of RM was compared with 2 radiologists' visual interpretation of CTE using receiver operating characteristic (ROC) curve analysis. RESULTS: In the training cohort, the area under the ROC curve (AUC) of RM for distinguishing moderate-severe from none-mild intestinal fibrosis was 0.888 (95% confidence interval [CI], 0.818-0.957). In the test cohort, the RM showed robust performance across 3 centers with an AUC of 0.816 (95% CI, 0.706-0.926), 0.724 (95% CI, 0.526-0.923), and 0.750 (95% CI, 0.560-0.940), respectively. Moreover, the RM was more accurate than visual interpretations by either radiologist (radiologist 1, AUC = 0.554; radiologist 2, AUC = 0.598; both, P < .001) in the test cohort. Decision curve analysis showed that the RM provided a better net benefit to predicting intestinal fibrosis than the radiologists. CONCLUSIONS: A CTE-based RM allows for accurate characterization of intestinal fibrosis in CD.
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