Fig. 2

Three TMB-associated radiomics features are selected. A Radiomics workflow for the study. The ROI of the tumor is segmented and reconstructed to extract high-dimensional radiomics features. B The unsupervised clustering heatmap shows all the radiomic features extracted from the tumor ROI of 62 NSCLC patients in the training set. C The LASSO regression was used to select the radiomics features associated with TMB. The left panel shows the tuning parameter (λ) of the LASSO regression model selected by the 10-fold cross-validation method based on the minimum criterion. The right panel shows the LASSO coefficient profile consisting of 1037 radiomics features. The dashed vertical upper x-axis represents the average number of radiomics features and the dashed vertical lower x-axis corresponds to a log(λ) value of − 2.117. D The Z-score values for three features of NSCLC patients between high TMB and low TMB. E The heatmap of the correlation between radiomics features and TMB in the training set