Features dimension reduction and selection
So far, we have constructed a CBR dataset containing 1738 multi-scale features (25 clinico-biological features, 11 conventional image features, and 1702 radiomics features) for all included patients. The processes of features dimension reduction and selection were performed using the classical supervised ML algorithms in the training set. Firstly, the features with intra- and inter-class correlation coefficients (ICC) < 0.8 were excluded due to the poor consistency and reproducibility. Then, we performed the mRMR algorithm to preliminarily narrow the range of redundant and irrelevant features, and selected the top 50 features. Finally, the Lasso algorithm with tenfold cross-validation was applied to further screen the optimal features for prediction model development.