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

Abstract 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.

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Publication Details

Journal
British journal of surgery
Published
2026-03-01
DOI
https://doi.org/10.1093/bjs/znag018.344
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
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article

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

Joël L. Lavanchy, Julia Wolleb, Christian A. Nebiker, Philippe Claude Cattin et al.
British journal of surgery
Pancreatic and Hepatic Oncology Research
article

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

Joël L. Lavanchy, Julia Wolleb, Christian A. Nebiker, Philippe Claude Cattin, B. Muller, Christoph Kümmerli, Stephanie Taha-Mehlitz, Jan Liechti, Julia Ruppel, Vincent Ochs, Giuseppe Kito Fusai, Sebastian Staubli
article en

Abstract

Abstract 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.

British journal of surgeryVol. 113(Supplement_2)
Royal Free London NHS Foundation Trust (GB), The Royal Free Hospital (GB), Institut de Recherche en Santé Digestive (FR), Department of Biomedicine Basel (CH), Kantonsspital Aarau (CH), University College London (GB)
Good health and well-being
Openalex Percentile: Top 5%
Pancreatic and Hepatic Oncology Research
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