用术前CT自动预测胰腺手术后胰瘘风险
Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography
- 构建端到端深度学习模型，从CT图像自动分割胰腺并分类风险
- 多种3D网络架构在预测胰瘘上表现良好，达到临床可用水平
- 为胰腺手术前风险评估提供可落地的智能工具，适合外科医生使用
术后胰瘘（POPF）是胰腺切除术后严重并发症，增加发病率、住院时间和医疗成本。本文提出一种全自动、端到端的深度学习流程——从胰腺分割到分类，基于术前CT扫描实现POPF风险的预估与分层。使用包含自动分割胰腺体积和手术结果的数据集，评估了多种模型架构，包括自研轻量级3D CNN基线（CNN3D）、R(2+1)D ResNet-18和ResNet-MC3-18。在多个3D网络架构上的评估显示具有良好的预测性能。该方法为胰腺特异性CT分类提供了临床实用工具与方法学基准，有助于提升胰腺手术前的决策质量。
原文摘要 · Abstract (English)
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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