Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and peritumoral radiomics with body composition

作者：Linxia Wu, Chunyuan Cen, Die Ouyang, Licai Zhang, Xin Li, Heshui Wu, Mingguang He, Ping Han, Wei Tan, Lei Chen, Chuansheng Zheng · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000003078 · 被引用次数：22 · 研究领域：Pancreatic and Hepatic Oncology Research、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is associated with a high rate of early recurrence (ER) after radical resection, which significantly affects long-term survival. Currently, no reliable system exists for predicting ER in these patients. This study aimed to develop a machine learning (ML) model combining intratumoral and peritumoral radiomic features with body composition to predict the ER risk in patients with PDAC following radical resection. MATERIALS AND METHODS: This study included patients with PDAC who underwent upfront surgery at four hospitals between June 2014 and December 2023. Preoperative clinical information, computed tomography (CT) images, and postoperative pathological data were collected. CT-quantified body composition was measured; radiomic features were extracted from the intratumoral and peritumoral regions. Six ML algorithms were used to develop predictive models, including radiomics, clinical, clinical-radiomics, and clinicopathological-radiomics models. The SHapley Additive exPlanations (SHAP) method was applied for model interpretability. RESULTS: A total of 589 patients were evaluated, including 320 patients (mean age: 60.4 ± 8.3 years; 191 men) in the training cohort, 138 patients (mean age: 60.7 ± 8.9 years; 84 men) in the internal validation cohort, and 131 patients (mean age: 61.7 ± 10.9 years; 76 men) in the external validation cohort. The intra-peri-radiomics model, based on the random forest algorithm, achieved the best perfor...