Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography

Ashok Choudhary; Chris Varghese; Leo Y. Li-Han; Frank G. Lee; Ellen L. Larson; Elizabeth B. Habermann; Cornelius A. Thiels; Hojjat Salehinejad

cs.CVMay 29, 2026

Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography

Authors: Ashok Choudhary, Chris Varghese, Leo Y. Li-Han, Frank G. Lee, Ellen L. Larson, Elizabeth B. Habermann, Cornelius A. Thiels, Hojjat Salehinejad

Organizations: Department of Surgery, Mayo Clinic, Rochester, MN, USA · Department of Surgery, University of Auckland, Auckland, NZ · Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA · Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA

Abstract

Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs. We present an automatic, end-to-end deep learning pipeline-from pancreatic segmentation to classification-for preoperative POPF risk estimation and stratification using preoperative CT scans. A data set with auto-segmented pancreas volumes and surgical outcomes was used to evaluate multiple architectures, including a custom lightweight 3D CNN baseline (CNN3D), R(2+1)D ResNet-18, and ResNet-MC3-18 models. Evaluation across multiple 3D architectures demonstrated promising predictive performance. This approach offers a clinically valuable tool and a methodological benchmark for pancreas-specific CT classification, supporting improved preoperative decision-making in pancreatic surgery.

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Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

Vincent Ochs, Christoph Kuemmerli, Florentin Bieder, Julia Wolleb+11

University of Basel, Department of Biomedical Engineering, 4123 Allschwil, Switzerland · Clarunis, University Digestive Health Centre, Basel, Switzerland · Department of General Surgery, Kantonsspital Aarau, Tellstrasse, 5000, Aarau, Switzerland · HPB and Liver Transplant Service, Royal Free Hospital, London, United Kingdom · Department of Visceral Surgery, Cantonal Hospital Basel-Land, Liestal, Switzerland

Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability. We introduce a fully automated multimodal deep learning framework that jointly analyzes 3D contrast enhanced CT and structured clinical information to classify patients into the three National Comprehensive Cancer Network (NCCN) resectability categories (upfront resectable, borderline resectable, locally advanced). The approach uses a Swin-UNETR backbone to obtain anatomy aware image representations through auxiliary segmentation of pancreas, tumor, and vascular structures. These features are fused with a compact clinical embedding derived from 17 routinely collected variables and processed by a lightweight classification head. Model training is guided by a dynamic multitask objective that adapts the balance between segmentation and classification based on current tumor Dice performance, promoting feature representations that remain both anatomically informed and discriminative.

Lesion Segmentation Non-Small Cell Lung Cancer Deep Learning Multimodal Evaluation

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Abstract

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