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已发表论文
利用肠超声图像通过影像组学和深度学习区分克罗恩病中炎症性和纤维性肠狭窄的比较研究
Authors Zhou R, Shi J, Qin J , Xue X, Zhou W , Zhao X, He X, Ma L, Zhou M, Guo C, Bai X , Zhu Q, Yang H
Received 13 June 2025
Accepted for publication 11 November 2025
Published 24 November 2025 Volume 2025:18 Pages 16399—16410
DOI https://doi.org/10.2147/JIR.S546832
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Nadia Andrea Andreani
Runing Zhou,1,* Jialin Shi,2,* Jing Qin,3,* Xiaowei Xue,4 Weixun Zhou,4 Xue Zhao,2 Xidong He,1 Li Ma,3 Mengyuan Zhou,3 Chenyi Guo,5 Xiaoyin Bai,1 Qingli Zhu,3 Hong Yang1
1Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 2School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, People’s Republic of China; 3Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 4Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 5Department of Electronic Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoyin Bai, Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China, Tel +86-10-69155005, Email baixiaoyin@pumch.cn Hong Yang, Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China, Tel +86-10-69155014, Email yangh@pumch.cn
Background: Intestinal strictures represent a significant complication in Crohn’s disease (CD), and accurate differentiation between inflammatory and fibrotic strictures is critical for effective treatment planning. Intestinal ultrasound (IUS) assists in diagnosing and monitoring strictures in CD, yet it faces limitations in distinguishing stricture types. This study aimed to compare radiomics-based and deep learning-based approaches for classifying bowel strictures using IUS images.
Methods: We conducted a retrospective analysis of 64 CD patients who underwent surgery between 2018 and 2023. A total of 87 ultrasound images were evaluated using radiomics and deep learning methodologies. We used 5-fold cross-validation, where in each fold, the training set was used for feature extraction and diagnostic model construction, and the test set was used to evaluate model performance. Model performances were assessed through accuracy, sensitivity, specificity, and the area under the ROC curve. Additionally, two experienced radiologists independently evaluated the nature of the strictures, and their assessments were compared with the models. Histopathological analysis via Masson’s trichrome staining served as the reference standard.
Results: Results indicated the deep learning-based model had superior performance, achieving an accuracy of 83.3%, sensitivity of 88.9%, and specificity of 77.8%, outperforming the radiomics-based approach, which achieved an accuracy of 67.0%. Although expert evaluations provided the highest specificity (86.7%), the deep learning model demonstrated overall superior accuracy compared to both radiomics and expert predictions. Class Activation Mapping highlighted effective feature identification, enhancing discrimination between inflammatory and fibrotic strictures.
Conclusion: This pioneering research applies deep learning and radiomics to intestinal ultrasound for the first time to distinguish the types of bowel strictures. The real-time visualization of deep learning attention maps (class activation maps) effectively highlights discriminative pathological regions and enhances interpretability, demonstrating strong potential to support clinical decision-making in intestinal stricture classification.
Keywords: Crohn’s disease, intestinal strictures, deep learning, radiomics, ultrasound imaging, artificial intelligence
1Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 2School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, People’s Republic of China; 3Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 4Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China; 5Department of Electronic Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoyin Bai, Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China, Tel +86-10-69155005, Email baixiaoyin@pumch.cn Hong Yang, Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China, Tel +86-10-69155014, Email yangh@pumch.cn
Background: Intestinal strictures represent a significant complication in Crohn’s disease (CD), and accurate differentiation between inflammatory and fibrotic strictures is critical for effective treatment planning. Intestinal ultrasound (IUS) assists in diagnosing and monitoring strictures in CD, yet it faces limitations in distinguishing stricture types. This study aimed to compare radiomics-based and deep learning-based approaches for classifying bowel strictures using IUS images.
Methods: We conducted a retrospective analysis of 64 CD patients who underwent surgery between 2018 and 2023. A total of 87 ultrasound images were evaluated using radiomics and deep learning methodologies. We used 5-fold cross-validation, where in each fold, the training set was used for feature extraction and diagnostic model construction, and the test set was used to evaluate model performance. Model performances were assessed through accuracy, sensitivity, specificity, and the area under the ROC curve. Additionally, two experienced radiologists independently evaluated the nature of the strictures, and their assessments were compared with the models. Histopathological analysis via Masson’s trichrome staining served as the reference standard.
Results: Results indicated the deep learning-based model had superior performance, achieving an accuracy of 83.3%, sensitivity of 88.9%, and specificity of 77.8%, outperforming the radiomics-based approach, which achieved an accuracy of 67.0%. Although expert evaluations provided the highest specificity (86.7%), the deep learning model demonstrated overall superior accuracy compared to both radiomics and expert predictions. Class Activation Mapping highlighted effective feature identification, enhancing discrimination between inflammatory and fibrotic strictures.
Conclusion: This pioneering research applies deep learning and radiomics to intestinal ultrasound for the first time to distinguish the types of bowel strictures. The real-time visualization of deep learning attention maps (class activation maps) effectively highlights discriminative pathological regions and enhances interpretability, demonstrating strong potential to support clinical decision-making in intestinal stricture classification.
Keywords: Crohn’s disease, intestinal strictures, deep learning, radiomics, ultrasound imaging, artificial intelligence