Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.

翻译：胰腺导管腺癌（PDAC）是一种高度致命的癌症，其肿瘤与血管的侵犯程度显著影响手术可切除性及患者总生存期。然而，现有预后预测方法未能明确且准确地探究肿瘤与邻近重要血管之间的关系。本文提出一种新颖的可学习神经距离，用于描述不同患者CT图像中肿瘤与血管的精确关系，并将其作为预后预测的主要特征。此外，与现有利用CNN或LSTM提取动态增强CT成像中肿瘤强化模式的模型不同，我们通过融合CNN与Transformer模块的局部与全局特征，改进了多期增强CT中动态肿瘤相关纹理特征的提取，进一步增强了跨多期CT图像的特征提取能力。我们在包含1070例PDAC患者的四中心数据集上对所提方法进行了广泛评估与对比，统计分析证实了其在由三个中心组成的外部测试集中的临床有效性。所开发的风险标志物是术前因素中对总生存期最强的预测因子，且具备与现有临床因素结合以筛选可能受益于新辅助治疗的高风险患者的潜力。