Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
BMC Med Imaging. 2020 Feb 3;20(1):11. doi: 10.1186/s12880-020-0418-1.
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.
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.
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.
Cox 比例风险模型（CPH）常用于临床研究中的生存分析。在定量医学成像（放射组学）研究中，CPH 在特征降维和建模方面发挥着重要作用。然而，CPH 模型的线性假设限制了其预后性能。在这项工作中，我们使用迁移学习，在可切除胰腺导管腺癌（PDAC）患者的术前 CT 图像上构建并测试了基于卷积神经网络（CNN）的生存模型。
与传统的基于 CPH 的放射组学方法相比，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表现更好，能够更好地拟合患者的生存模式。
基于 CNN 的生存模型在 PDAC 预后方面优于基于 CPH 的放射组学模型。该方法基于 CT 图像提供了更好的生存模式拟合，克服了传统生存模型的局限性。
