FIGURE 2.

A, Setup of the benchmark pipeline for the analysis of prognostic information in different (radiomics) feature sets regarding overall survival in patients with hepatic metastases due to colorectal cancer. The basis of the machine learning pipelines is given by the 3 algorithms—a random survival forest (RSF), a regularized generalized linear model (glmnet), and a gradient tree-boosting technique (xgboost)—combined with varying configurations of preprocessing (feature selection, principal component analysis yes/no) and tuning yes/no, evaluated with respect to the performance metric integrated Brier score (IBS). B, Detailed look into the tuning (k = 5)/performance (k = 10) loop for k-fold cross-validation (CV). Preprocessing steps in the pipeline construction and the corresponding learner development are based on the training folds, whereas model evaluation is based on the test fold.