Preoperative prediction of early recurrence in pancreatic cancer: A novel clinical–radiomics model

Abstract Early postoperative recurrence critically impacts pancreatic ductal adenocarcinoma prognosis, yet comprehensive preoperative prediction models remain underexplored. In this two‐center retrospective study of 895 treatment‐naïve PDAC patients who underwent direct resection (training n = 567; internal validation n = 241; external validation n = 87), we defined early recurrence as tumor relapse within 6 months of surgery. We first built a clinical model using logistic regression to select clinical variables and a radiomics model by applying LASSO regression to features extracted from preoperative CT images, then combined these into an integrated clinical–radiomics model via logistic regression. Of the 895 patients (64.4% male; mean age 64.4 ± 8.7 years), 213 (23.8%) experienced early recurrence. Four clinical variables (gender, CA125, radiologic N stage, adjuvant treatment) and 29 radiomics features were selected for the final model, which achieved area under the curve values of 0.862 (95% CI 0.828–0.896) in the training cohort, 0.843 (0.785–0.901) in internal validation, and 0.848 (0.748–0.949) in external validation—each outperforming either the clinical or radiomics model alone. Stratified analyses confirmed robustness across subgroups, and patients classified as high risk by the model had significantly shorter disease‐free and overall survival (both p < .001). This clinical–radiomics model offers a preoperative tool to identify PDAC patients at high risk of early postoperative recurrence, thereby supporting personalized treatment planning beyond immediate surgery.