Additional file 1 of A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer

Additional file 1: Table S1. The formulae for the calculation of primary radiomic features. Table S2. Grid search results of DeepSurv hyper-parameters. Table S3. Comparisons of clinical characteristics between training and test sets. Table S4. Characteristics of clinical laboratory test. Table S5. Identified features for the model training in each DeepSurv model. Figure S1. The architecture of applied DeepSurv model. Figure S2. Schematic diagram of predictive risk-of-progression period in DeepSurv model