Fig. 3
From: CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer
Development of the radiomics model and its performance. A The radiomics dynamic nomogram was constructed using the three radiomics features, Flatness (shape of original feature), Minimum (first order of wavelet features) and Autocorrelation (GLCM). B Histogram of feature weights for logistic regression. C Pearson’s rank correlation among the three radiomic features. D ROC curve of the CT-based nomogram to predict TMB in the training set (Left panel). The spearman correlation test between TMB values and TIDE scores. Red dots indicate the NSCLC samples predicted high TMB by the radiomics model. Blue dots indicate the NSCLC samples predicted low TMB by the radiomics model. E ROC of the radiomics predictive model in two validation sets. F The Sankey diagram shows the patients correctly and incorrectly classified by the radiomics prediction model in the training dataset and two validation datasets