A CT-Based Interpretable Machine Learning Model for Preoperative Prediction of Pancreatic Neuroendocrine Tumor Aggressiveness

Rong Kong¹Shunzu Lu¹Yugui Huang¹Siyu Tan¹Chunxia Zhu¹Guowei Chen¹Mingrui Yang¹Ying Liu¹Qixin Wu²Peng Peng^1*

• ¹The First Affiliated Hospital of Guangxi Meidical University, Nanning, China
• ²The People's Hospital of Chongzuo, Chongzuo, China

Objectives: This study aimed to develop and validate an interpretable machine learning (ML) model based on structured preoperative CT features for non-invasive prediction of pancreatic neuroendocrine Tumors (PNETs) aggressiveness. Methods: This retrospective study included 112 patients with PNETs who underwent contrast-enhanced abdominal CT. Patients were randomly assigned to training and validation cohorts. Clinical data and CT features were analysed using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to identify independent risk factors. Multiple ML models were evaluated to determine the optimal classifier. Model performance was assessed using receiver operating characteristic and calibration curves, and decision curve analysis. Shapley Additive Explanations (SHAP) quantified feature importance for interpretable risk prediction. Results: A total of 112 patients were evaluated, including 80 (mean age± standard deviation, 47 ± 13 years; 36 males) ) in the training set and 32 ( 48 ± 15 years; 12 males) in the validation set. Tumour shape, necrotic changes, arterial relative enhancement ratio, and enhancement pattern independently predicted PNETs aggressiveness. The logistic regression model demonstrated excellent discrimination, achieving an area under the curve of 0.952 (95% CI: 0.952 (0.909–0.994) in the training cohort and 0.972 (95% CI 0.927–1.000) in the validation cohort. SHAP summary and force plots facilitated global and local model interpretation. Conclusion: The Interpretable ML model based on CT features could serve as a preoperative, noninvasive, and precise evaluation tool to differentiate aggressive and non-aggressive PNETs, facilitating personalized clinical management and potentially improving patient outcomes.