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

Abstract

IMPORTANCE There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC). OBJECTIVESTo 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 learningbased 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

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