发表机构
University of Basel; Clarunis, University Digestive Health Centre; Kantonsspital Aarau; Royal Free Hospital（巴塞尔大学; 克拉鲁尼斯大学消化健康中心; 阿劳州立医院; 皇家自由医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用深度学习框架联合分析CT与临床信息，对胰腺癌可切除性分类。通过Swin-UNETR主干获取图像特征，融合临床嵌入，经动态多任务目标训练，能将患者分入NCCN的三种可切除性类别，提高评估准确性。
AI 中文摘要
准确确定胰腺导管腺癌（PDAC）的可切除性依赖于在CT成像上评估肿瘤与胰腺周围主要血管的相互作用，但专家评估往往存在很大差异。我们引入了一个全自动多模态深度学习框架，该框架联合分析三维对比增强CT和结构化临床信息，将患者分为国家综合癌症网络（NCCN）的三种可切除性类别（直接可切除、边界可切除、局部晚期）。该方法使用Swin-UNETR主干，通过胰腺、肿瘤和血管结构的辅助分割来获得具有解剖学意识的图像表示。这些特征与从17个常规收集的变量中得出的紧凑临床嵌入相结合，并由一个轻量级分类头进行处理。模型训练由动态多任务目标指导，该目标根据当前肿瘤Dice性能调整分割和分类之间的平衡，促进既具有解剖学信息又具有判别力的特征表示。
英文摘要
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.
