Radiomics for preoperative pancreatic ductal adenocarcinoma risk stratification: Cross-population validation, multidimensional integration, challenges, and future directions.

Abstract

This editorial critically evaluated Liu et al's recent retrospective analysis of 283 Chinese patients with resectable pancreatic ductal adenocarcinoma (PDAC) that validated a preoperative computed tomography-based risk scoring system originally developed in South Korea. The scoring system incorporated five parameters: (1) Tumor size; (2) Portal venous phase density; (3) Necrosis; (4) Peripancreatic infiltration; and (5) Suspected metastatic lymph nodes. While demonstrating satisfactory recurrence prediction capability without requiring complex technologies, thereby supporting clinical utility in Chinese populations, the study exhibited notable limitations. Most analyzed patients lacked neoadjuvant chemotherapy exposure, resulting in underrepresentation of low-risk subgroups. Additionally, the short follow-up duration potentially compromised long-term prognostic assessment. Contemporary advances in radiomics coupled with machine learning have enhanced multimodal data integration for PDAC management. However, clinical implementation continues to confront challenges including variability in imaging parameters, incomplete understanding of molecular underpinnings, and confounding treatment effects. Future investigations should prioritize developing multidimensional predictive frameworks that synergize radiographic, molecular, and clinical data. Prospective multicenter validation and artificial intelligence-powered real-time risk stratification systems represent essential steps to overcome current barriers in precision medicine translation, ultimately advancing personalized therapeutic strategies for PDAC.