Development and validation of machine learning models integrating spectral computed tomography-derived three-dimensional quantitative parameters for predicting histopathological grade in pancreatic ductal adenocarcinoma - Song, Zuhua; Li, Zongwen; Wang, Xinwei; Huang, Jie; Chen, Yuwei; Zhang, Xiaodi; Tang, Zhuoyue (Journal of Gastrointestinal Oncology; Vol 17, No 2 (April 30, 2026): Journal of Gastrointestinal Oncology, 2026)
Pancreatic ductal adenocarcinoma (PDAC) carries a dismal prognosis, with histopathological grade closely linked to treatment selection, therapeutic response, and clinical outcomes. However, tumor grading currently relies on postoperative pathological assessment of resected specimens, limiting its utility in guiding preoperative clinical decision-making. The objective of this study was to assess the potential of integrating spectral computed tomography (CT)-derived three-dimensional (3D) quantitative parameters with clinical data using multiple machine learning (ML) algorithms to preoperatively predict histopathological grade in patients with PDAC.