Abstract
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. Clinical and imaging data from 271 patients who had undergone enhanced CT scans after first-episode acute pancreatitis from March 2017-June 2023 were retrospectively analyzed. Patients were classified into PPDM-A (n = 109) and non-PPDM-A groups (n = 162), and split into training (n = 189) and testing (n = 82) cohorts at a 7:3 ratio. 1223 radiomic features were extracted from CT images in the plain, arterial and venous phases, respectively. The radiomics model was developed based on the optimal features retained after dimensionality reduction, utilizing the extreme gradient boosting (XGBoost) algorithm. Five-fold cross-validation of the model was used to assess the performance of the model in the training and testing cohorts. The clinical performance of the model was assessed through a decision curve analysis, while insight into the predictions derived from this model was derived from Shapley additive explanations (SHAP). The final model incorporated five key radiomic features, and achieved area under the curve values in the training and testing cohorts of 0.947 (95% CI 0.915-0.979) and 0.901 (95% CI 0.838-0.964), respectively. SHAP analyses indicated that textural features were key features relevant to the prediction of PPDM-A incidence. The interpretable CT radiomics-based model developed in this study demonstrated good performance, enabling timely and effective interventions with the potential to improve patient outcomes.