Radiomics-based interpretable machine learning model for identifying high-risk patients with small bowel obstruction: a multicenter study.
5 affiliations
- 1Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University Quzhou People's Hospital, Quzhou, China.
- 2Department of Radiology, Quzhou Kecheng People's Hospital, Quzhou, China.
- 3Department of Radiology, Quzhou TCM Hospital at the Junction of Four Provinces, Quzhou, China.
- 4Department of Medical Affairs, Pharmaceutical Diagnosis, GE Healthcare, Hangzhou, China.
- 5Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University Quzhou People's Hospital, Quzhou, China.
Abstract
Background
Early identification of high-risk small bowel obstruction remains clinically challenging, especially when prompt intervention is needed to prevent bowel ischemia and related adverse outcomes.Methods
In this multicenter retrospective study, 274 patients with small bowel obstruction from three hospitals were included. Patients from Hospital A were randomly divided into training and test cohorts, and patients from the other two hospitals formed an external validation cohort. Radiomics features were extracted from venous-phase contrast-enhanced CT images, and logistic regression, naive Bayes, Extra Trees, Light Gradient Boosting Machine (LightGBM), and multilayer perceptron models were developed. Independent clinical predictors were identified and integrated with radiomics features to build a combined model.Results
LightGBM showed the most stable internal performance, with AUC values of 0.934 and 0.870 in the training and test cohorts, respectively. Lower diastolic blood pressure and bowel wall thickening were independent predictors of high-risk status. The combined model achieved AUC values of 0.942, 0.867, and 0.803 in the training, test, and external validation cohorts, respectively, with an external validation accuracy of 0.833 and sensitivity of 0.946.Conclusion
CT-based radiomics can identify patients with small bowel obstruction at high risk, and a combined model integrating radiomics and clinical predictors may support earlier risk stratification and intervention.
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BibTeX
@article{zhang2026radiomicsbased,
title = {{Radiomics-based interpretable machine learning model for identifying high-risk patients with small bowel obstruction: a multicenter study.}},
author = {Guozheng Zhang and Xiaohui Liu and Weitao Huang and Xisong Zhu and Cong Xia and Hua Xia and Yuguo Wei and Xiaowei Han and Danqiong Wang},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1848334},
url = {https://europepmc.org/article/MED/42921497},
note = {CC BY},
}
RIS
TY - JOUR
TI - Radiomics-based interpretable machine learning model for identifying high-risk patients with small bowel obstruction: a multicenter study.
AU - Guozheng Zhang
AU - Xiaohui Liu
AU - Weitao Huang
AU - Xisong Zhu
AU - Cong Xia
AU - Hua Xia
AU - Yuguo Wei
AU - Xiaowei Han
AU - Danqiong Wang
PY - 2026
DA - 2026/10/09
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1848334
UR - https://europepmc.org/article/MED/42921497
AB - Background Early identification of high-risk small bowel obstruction remains clinically challenging, especially when prompt intervention is needed to prevent bowel ischemia and related adverse outcomes.Methods In this multicenter retrospective study, 274 patients with small bowel obstruction from three hospitals were included. Patients from Hospital A were randomly divided into training and test cohorts, and patients from the other two hospitals formed an external validation cohort. Radiomics features were extracted from venous-phase contrast-enhanced CT images, and logistic regression, naive Bayes, Extra Trees, Light Gradient Boosting Machine (LightGBM), and multilayer perceptron models were developed. Independent clinical predictors were identified and integrated with radiomics features to build a combined model.Results LightGBM showed the most stable internal performance, with AUC values of 0.934 and 0.870 in the training and test cohorts, respectively. Lower diastolic blood pressure and bowel wall thickening were independent predictors of high-risk status. The combined model achieved AUC values of 0.942, 0.867, and 0.803 in the training, test, and external validation cohorts, respectively, with an external validation accuracy of 0.833 and sensitivity of 0.946.Conclusion CT-based radiomics can identify patients with small bowel obstruction at high risk, and a combined model integrating radiomics and clinical predictors may support earlier risk stratification and intervention.
SN - 2296-858X
ER -