Development and Validation of a Deep Learning Model for Non–Small Cell Lung Cancer Survival

作者：Yunlang She, Zhuochen Jin, Junqi Wu, Jiajun Deng, Lei Zhang, Hang Su, Gening Jiang, Haipeng Liu, Dong Xie, Nan Cao, Yijiu Ren, Chang Chen · 发表于：JAMA Network Open · 年份：2020 · DOI：10.1001/jamanetworkopen.2020.5842 · 被引用次数：278 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Ferroptosis and cancer prognosis

Importance: There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC). Objectives: To compare the performances of DeepSurv, a deep learning survival neural network with a tumor, node, and metastasis staging system in the prediction of survival and test the reliability of individual treatment recommendations provided by the deep learning survival neural network. Design, Setting, and Participants: In this population-based cohort study, a deep learning-based algorithm was developed and validated using consecutive cases of newly diagnosed stages I to IV NSCLC between January 2010 and December 2015 in a Surveillance, Epidemiology, and End Results database. A total of 127 features, including patient characteristics, tumor stage, and treatment strategies, were assessed for analysis. The algorithm was externally validated on an independent test cohort, comprising 1182 patients with stage I to III NSCLC diagnosed between January 2009 and December 2013 in Shanghai Pulmonary Hospital. Analysis began January 2018 and ended June 2019. Main Outcomes and Measures: The deep learning survival neural network model was compared with the tumor, node, and metastasis staging system for lung cancer-specific survival. The C statistic was used to assess the performance of models. A user-friendly interface was provided to facilitate the survival predictions and treatment recommendations of the deep learning survival neural net...