Interpretable CT-based radiomics nomogram integrating clinical and semantic features for preoperative grading of pancreatic neuroendocrine neoplasms: a shap-based analysis.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
- Department of Geriatric Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. Electronic address: [email protected].
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. Electronic address: [email protected].
Abstract
Preoperative grading of pancreatic neuroendocrine neoplasms (PNENs) is crucial for personalized management. This study aimed to develop and validate an interpretable machine-learning framework based on CT imaging to predict PNENs pathological grades. We retrospectively analyzed 125 patients with pathologically confirmed PNENs, divided into training and test cohorts. A comprehensive radiomics pipeline was implemented, involving the extraction of high-dimensional features from CT images. We constructed three predictive models: a clinical model, a radiomics model, and a fusion model. To ensure methodological robustness, seven machine-learning algorithms were employed and cross-validated to identify the optimal classifier. Furthermore, the SHAP (SHapley Additive exPlanations) method was utilized to decode the "black box" of the radiomics signature. The fusion model-integrating CT radiomic signatures with semantic hallmarks-consistently outperformed the standalone clinical and radiomics models, achieving a superior AUC of 0.891 in the validation cohort. SHAP analysis revealed that microscopic texture complexity (e.g., Zone Entropy) and macro-geometric irregularity (e.g., Sphericity) were the dominant predictors, aligning with the biological aggressiveness of high-grade lesions. By integrating multi-dimensional CT features with a rigorous machine-learning consensus, our study provides a highly accurate and interpretable "virtual biopsy" tool.