Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images
Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images
复制标题
使用多中心计算机断层扫描图像进行生存风险预测的知识引导多任务注意网络
DOI：
10.1016/j.neunet.2022.04.027
复制
发表时间：
2022-04
期刊：
影响因子：
7.8
通讯作者：
Jie Tian
中科院分区：
文献类型：
--
作者：
Liwen Zhang;Lianzhen Zhong;Cong Li;Wenjuan Zhang;Chaoen Hu;Di Dong;Zaiyi Liu;Junlin Zhou;Jie Tian
术前基于CT图像准确预测人类癌症的总生存（OS）风险对于个性化治疗具有重要意义。深度学习方法已被广泛用于改进操作系统风险的自动预测。然而，现有方法对OS风险预测的准确性存在一定的限制。为了方便获取生存相关信息，我们提出了一种新的知识引导的多任务网络，该网络具有定制的注意力模块，可同时用于OS风险预测和临床分期预测。该网络利用包含在多个学习任务中的有用信息来提高对操作系统风险的预测。三个多中心数据集，包括两个459例患者的胃癌数据集和一个422例患者的美国公开肺癌数据集，被用来评估我们提出的网络。结果表明，我们提出的网络可以通过捕获和共享来自其他临床阶段预测的信息来提高其性能。我们的方法以最高的几何度量优于最先进的方法。此外，我们的方法显示出更好的预后价值，将患者分为高风险和低风险组的风险比最高。因此，我们提出的方法可能被开发为改进个性化治疗的潜在工具。
Accurate preoperative prediction of overall survival (OS) risk of human cancers based on CT images is greatly significant for personalized treatment. Deep learning methods have been widely explored to improve automated prediction of OS risk. However, the accuracy of OS risk prediction has been limited by prior existing methods. To facilitate capturing survival-related information, we proposed a novel knowledge-guided multi-task network with tailored attention modules for OS risk prediction and prediction of clinical stages simultaneously. The network exploits useful information contained in multiple learning tasks to improve prediction of OS risk. Three multi-center datasets, including two gastric cancer datasets with 459 patients, and a public American lung cancer dataset with 422 patients, are used to evaluate our proposed network. The results show that our proposed network can boost its performance by capturing and sharing information from other predictions of clinical stages. Our method outperforms the state-of-the-art methods with the highest geometrical metric. Furthermore, our method shows better prognostic value with the highest hazard ratio for stratifying patients into high- and low-risk groups. Therefore, our proposed method may be exploited as a potential tool for the improvement of personalized treatment.