An explainable AI workflow integrating automated volumetric body composition analysis for predicting pathological grading of gastroenteropancreatic neuroendocrine neoplasms: a multicenter cohort study

Background: Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are heterogeneous tumors with rising incidence, necessitating precise preoperative grading for treatment planning. Existing imaging techniques and endoscopic biopsies often fall short due to insufficient markers and tissue samples. Body composition influences tumor biology, yet traditional 2D assessments are time-consuming and lack objectivity. This study aimed to develop a rapid non-invasive predictive model by integrating automatic segmented abdominal volumetric body composition with machine learning to differentiate between low-grade and high-grade GEP-NENs. Materials and Methods: This multicenter retrospective cohort study enrolled 633 patients with GEP-NENs from three institutions. Patients were divided into: Training set (n = 403) and internal validation (n = 174) (7:3 ratio from Hospital 1); test set (n = 56 from 2 other hospitals). An nnUNetv2-based automatic segmentation algorithm for abdominal fat tissue and skeletal muscle on arterial-phase CT was applied. Visceral fat index, subcutaneous fat index, intermuscular fat index and skeletal muscle index were calculated. Features with a P -value < 0.05 were selected using univariate logistic regression and included in the prediction model built using the extreme gradient boosting algorithm. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were performed to evaluate the utility of the model. SHapley Additive exPlanations (SHAP) was conducted to enhance model interpretability and visualization. Results: The automatic segmentation achieved a Dice coefficient of 0.98. For pathological grading, the model built using body composition parameters achieved an AUC of 0.863 in the training set, 0.750 in the validation set, and 0.717 in the test set. SHAP analysis revealed that the relative intermuscular adipose tissue (rIMAT) contributed the most among the body composition parameters to the model decision-making, and rIMAT levels were higher in P53-mutant and CK19-positive cases compared to negative cases. Conclusions: Auto-segmented abdominal body composition combined with a machine learning-based model could provide an assisted, non-invasive tool for predicting pathological grade in GEP-NENs.