A nomogram based on CT deep learning signature

Xianyue Quan Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People's Republic of China Tel/Fax 86-2061643114 Email quanxianyue2014@163.com Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n 231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness.