Lin T, Mai J, Yan M, et al. - In resected non-small cell lung cancer (NSCLC) patients, researchers sought to construct and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival. Researchers extracted a total of 1,792 deep learning characteristics from non-enhanced and venous-phase CT images for each NSCLC patient in the training cohort (n=231). They evaluated the performance of the nomogram by discrimination, calibration, and clinical usefulness. This study’s findings demonstrated that deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients. The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability and a good calibration, which was validated in external validation cohort.