A clinically validated 3D deep learning approach for quantifying vascular invasion in pancreatic cancer.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Radiology, Wuxi No.2 People's Hospital (Jiangnan University Medical Center), Wuxi, China.
- Department of Nuclear Medicine, Tangshan People's Hospital, Tangshan, Hebei Province, China.
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu Province, China.
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- The Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China. [email protected].
Abstract
Vascular invasion assessment is critical for surgical planning in pancreatic ductal adenocarcinoma (PDAC). Current CT-based assessments often rely on radiologists' subjective 2D interpretations, which may not capture the continuous, three-dimensional tumor-vessel interactions and multiple vessel involvement, both essential for accurate preoperative evaluation. PAN-VIQ (Pancreatic Vascular Invasion Quantifier) is an automated deep learning framework to quantify tumor-vessel interactions from contrast-enhanced CT scans. It enables segmentation of pancreatic tumors and five critical vessels: celiac artery (CA), common hepatic artery (CHA), superior mesenteric artery (SMA), superior mesenteric vein (SMV), and portal vein (PV), quantifying vascular involvement through 3D encasement angles. PAN-VIQ was trained and internally validated on 2130 cases, and subsequently prospectively tested in 202 patients. External validation showed accuracies exceeding 90%. In prospective evaluation, the model outperformed junior radiologists and matched senior radiologists in accuracy and recall. These results underscore potential of PAN-VIQ to standardize vascular invasion assessment and reduce interobserver variability.