BACKGROUND Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. RESULTS The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients' survival patterns. CONCLUSIONS The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.

中文翻译：

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基于CNN的胰腺导管腺癌生存模型在医学影像中的应用。

背景技术Cox比例风险模型（CPH）通常用于临床研究中以进行生存分析。在定量医学成像（放射线学）研究中，CPH在特征缩减和建模中起着重要作用。但是，CPH模型的基本线性假设限制了其预后性能。在这项工作中，使用转移学习，建立了基于卷积神经网络（CNN）的生存模型，并在可切除的胰腺导管腺癌（PDAC）患者的术前CT图像上进行了测试。结果所提出的基于CNN的生存模型在一致性指数和预测准确性指数方面优于传统的基于CPH的放射学方法，从而更适合患者的生存模式。结论所提出的基于CNN的生存模型在PDAC的预后方面优于基于CPH的放射线。这种方法为基于CT图像的生存模式提供了更好的解决方案，并克服了传统生存模型的局限性。