Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging

Abstract Objectives This study aimed to develop interpretable deep learning (DL) and radiomics models using endoscopic ultrasound (EUS) images to differentiate insulinomas from nonfunctional pancreatic neuroendocrine tumors (NF-PNETs). Methods The retrospective analysis comprised 115 patients, including 61 with insulinomas and 54 with NF-PNETs, all confirmed through pathological examination. The patient cohort was divided into training and test groups. From standardized EUS images, a total of 512 DL features and 107 radiomics features were extracted. LASSO regression was employed to identify non-zero coefficient features from both the DL and radiomics datasets. Subsequently, four machine learning algorithms were utilized to construct predictive models. The optimal DL and radiomics models were then integrated into a nomogram for enhanced predictive capability. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) provided model interpretability. Results The ExtraTrees DL and radiomics models demonstrated exceptional performance. The integrated nomogram yielded AUC values of 0.978 for the training group and 0.842 for the test group. Calibration curves and decision curve analysis corroborated the high accuracy and clinical utility of the models. Grad-CAM identified tumor margins and heterogeneity as significant features in the DL model, whereas SHAP analysis highlighted texture patterns in the radiomics data. The nomogram effectively facilitated visual simplification of risk stratification. Conclusions The interpretable DL-radiomics nomogram exhibited significant potential in differentiating insulinomas from NF-PNETs using EUS. This methodology improves diagnostic accuracy and informs clinical decision-making.