Fig. 1

Flowchart of the DL model to estimate response. The CE-CT image databases were collected from 229 patients with NSCLC across six centers. CE-CT images were masked and prepared for DL model training. Images were trained to predict responses using the three-dimensional ResNet 50 model in the training cohort. The DL model was validated in the validation cohort. The OS and PFS were further analyzed in the two cohorts. Grad-CAM was visualized among six patients, including those with and without response to CCRT. The association between biological signaling pathways and the DL model was explored in patients with NSCLC. CCRT, concurrent chemoradiotherapy; CE-CT, contrast-enhanced computed tomography; DL, deep learning; Grad-CAM, gradient-weighted class activation mapping; NSCLC, non-small-cell lung carcinoma; OS, overall survival; PFS, progression-free survival