Wang Jianyong, Chen Nan, Guo Jixiang, Xu Xiuyuan, Liu Lunxu, Yi Zhang
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Department of Thoracic Surgery, West China Hospital and West China School of Medicine, Sichuan University, Chengdu, China.
Front Oncol. 2021 Jan 20;10:588990. doi: 10.3389/fonc.2020.588990. eCollection 2020.
Survival analysis is important for guiding further treatment and improving lung cancer prognosis. It is a challenging task because of the poor distinguishability of features and the missing values in practice. A novel multi-task based neural network, SurvNet, is proposed in this paper. The proposed SurvNet model is trained in a multi-task learning framework to jointly learn across three related tasks: input reconstruction, survival classification, and Cox regression. It uses an input reconstruction mechanism cooperating with incomplete-aware reconstruction loss for latent feature learning of incomplete data with missing values. Besides, the SurvNet model introduces a context gating mechanism to bridge the gap between survival classification and Cox regression. A new real-world dataset of 1,137 patients with IB-IIA stage non-small cell lung cancer is collected to evaluate the performance of the SurvNet model. The proposed SurvNet achieves a higher concordance index than the traditional Cox model and Cox-Net. The difference between high-risk and low-risk groups obtained by SurvNet is more significant than that of high-risk and low-risk groups obtained by the other models. Moreover, the SurvNet outperforms the other models even though the input data is randomly cropped and it achieves better generalization performance on the Surveillance, Epidemiology, and End Results Program (SEER) dataset.
生存分析对于指导进一步治疗和改善肺癌预后至关重要。由于特征的可区分性差以及实际中的缺失值，这是一项具有挑战性的任务。本文提出了一种基于多任务的新型神经网络SurvNet。所提出的SurvNet模型在多任务学习框架中进行训练，以跨三个相关任务联合学习：输入重建、生存分类和Cox回归。它使用输入重建机制与不完全感知重建损失协作，对具有缺失值的不完全数据进行潜在特征学习。此外，SurvNet模型引入了一种上下文门控机制来弥合生存分类和Cox回归之间的差距。收集了一个包含1137例IB-IIA期非小细胞肺癌患者的新真实世界数据集，以评估SurvNet模型的性能。所提出的SurvNet比传统的Cox模型和Cox-Net具有更高的一致性指数。SurvNet获得的高风险组和低风险组之间的差异比其他模型获得的高风险组和低风险组之间的差异更显著。此外，即使输入数据被随机裁剪，SurvNet也优于其他模型，并且它在监测、流行病学和最终结果计划（SEER）数据集上实现了更好的泛化性能。
