Development of radiomics signatures
REGRESSION, LIVER METASTASIS
Before screening radiomics features, all features were normalized by Z-score normalization. Least absolute shrinkage and selector operation (LASSO), random forest (RF) algorithm (the top 20 candidates), and principal component analysis (PCA) were used to filter the most useful radiomics features from high-dimensional imaging data from T1w, T2w, and CE-T1w images. Then supporting vector machine (SVM) model and logistic regression (LR) model were used to establish prediction models. The best model to predict liver metastasis was selected among LASSO-SVM, LASSO-LR, RF-SVM, RF-LR, PCA-SVM, and PCA-LR models.