Radiomics-based interpretable machine learning model for identifying high-risk patients with small bowel obstruction: a multicenter study.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University Quzhou People's Hospital, Quzhou, China.
- Department of Radiology, Quzhou Kecheng People's Hospital, Quzhou, China.
- Department of Radiology, Quzhou TCM Hospital at the Junction of Four Provinces, Quzhou, China.
- Department of Medical Affairs, Pharmaceutical Diagnosis, GE Healthcare, Hangzhou, China.
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University Quzhou People's Hospital, Quzhou, China.
Abstract
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. 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. 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. 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.