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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.31539 |
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| _version_ | 1866917548227821568 |
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| author | Choudhary, Ashok Varghese, Chris Li-Han, Leo Y. Lee, Frank G. Larson, Ellen L. Habermann, Elizabeth B. Thiels, Cornelius A. Salehinejad, Hojjat |
| author_facet | Choudhary, Ashok Varghese, Chris Li-Han, Leo Y. Lee, Frank G. Larson, Ellen L. Habermann, Elizabeth B. Thiels, Cornelius A. Salehinejad, Hojjat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31539 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography Choudhary, Ashok Varghese, Chris Li-Han, Leo Y. Lee, Frank G. Larson, Ellen L. Habermann, Elizabeth B. Thiels, Cornelius A. Salehinejad, Hojjat Computer Vision and Pattern Recognition Machine Learning Quantitative Methods 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. |
| title | Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography |
| topic | Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2605.31539 |