SRS388 - Pancreatic resectability evaluation through deep imaging with computed tomography in pancreatic cancer

Introduction Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality of solid malignancies (Sung H, CA Cancer J Clin). Surgical resection is the only curative option (NCCN Guidelines, 2019), yet accurate determination of resectability is difficult. Defined by vascular involvement, assessment suffers from low inter-observer agreement. Automated deep learning may provide standardized, reproducible support for operability assessment. Methods We developed an end-to-end multimodal pipeline to predict PDAC operability (resectable, borderline, irresectable) by integrating contrast-enhanced CT and clinical data. CT scans were processed using a SwinUNETR backbone. The encoder produced imaging features, while the decoder generated 16-class segmentations of pancreas, tumor, and vessels. Seventeen clinical variables underwent projection into an embedding. Imaging and tabular embeddings were concatenated and classified via a multilayer perceptron. Training used a multi-task loss combining segmentation (Dice, weighted cross-entropy, focal) and classification (weighted cross-entropy) with adaptive weighting. Evaluation used stratified 5-fold cross-validation and grid search optimization. Results Among 159 patients, 85 (53.5%) were resectable, 47 (29.5%) borderline, and 27 (17%) irresectable. Cross-validation yielded a mean Dice of 75.2% across 16 classes, including 70.4% for tumor, 69.7% for the superior mesenteric artery, and 65.1% for the vein. Operability prediction reached 85.1% accuracy, exceeding prior methods (70–88%) on smaller datasets. Conclusions Our model shows feasibility for automated PDAC operability prediction. By leveraging segmentation as an auxiliary task, it learns anatomically relevant features enhancing classification. Unlike segmentation-only approaches (Viviers, Bereska), our pipeline fuses imaging and clinical data. Future work includes multi-institutional validation and a prospective clinical trial via Pancreasgroup.org.