A CT-Based Deep Learning Radiomics Nomogram for Early Recurrence Prediction in Pancreatic Cancer: A Multicenter Study.

Abstract

Early recurrence (ER) following curative-intent surgery remains a major obstacle to improving long-term outcomes in patients with pancreatic cancer (PC). The accurate preoperative prediction of ER could significantly aid clinical decision-making and guide postoperative management. A retrospective cohort of 493 patients with histologically confirmed PC who underwent resection was analyzed. Contrast-enhanced computed tomography (CT) images were used for tumor segmentation, followed by radiomics and deep learning feature extraction. In total, four distinct feature selection algorithms were employed. Predictive models were constructed using random forest (RF) and support vector machine (SVM) classifiers. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC). A comprehensive nomogram integrating feature scores and clinical factors was developed and validated. Among all of the constructed models, the Inte-SVM demonstrated superior classification performance. The nomogram, incorporating the Inte-feature score, CT-assessed lymph node status, and carbohydrate antigen 19-9 (CA19-9), yielded excellent predictive accuracy in the validation cohort (AUC = 0.920). Calibration curves showed strong agreement between predicted and observed outcomes, and decision curve analysis confirmed the clinical utility of the nomogram. A CT-based deep learning radiomics nomogram enabled the accurate preoperative prediction of early recurrence in patients with pancreatic cancer. This model may serve as a valuable tool to assist clinicians in tailoring postoperative strategies and promoting personalized therapeutic approaches.