用深度学习自动判断胰腺癌是否可切除，提升诊断一致性。
Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning
- 融合CT影像与临床数据，端到端分类三种可切除状态。
- 动态多任务训练使分割与分类性能协同优化，提升准确性。
- 适合临床医生辅助决策，尤其关注胰腺癌手术评估者。
精准判断胰腺导管腺癌（PDAC）是否可切除，依赖于对肿瘤与主要胰周血管关系的影像学评估，但专家判断常存在显著差异。本文提出一种全自动多模态深度学习框架，联合分析3D增强CT与结构化临床信息，将患者分类为三大国家综合癌症网络（NCCN）可切除类别（即刻可切除、边缘可切除、局部进展）。该方法采用Swin-UNETR作为主干网络，通过胰腺、肿瘤及血管结构的辅助分割获取解剖感知的图像表征；这些特征与17个常规临床变量生成的紧凑嵌入向量融合，并经轻量级分类头处理。模型训练采用动态多任务目标，根据当前肿瘤Dice得分自适应调节分割与分类之间的权重，从而促进兼具解剖一致性与判别力的特征表示。
原文摘要 · Abstract (English)
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.
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