Research Paper Volume 13, Issue 7 pp 9960—9975

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

(A) The predictive performance of radiomics features for somatic mutations and molecular subtypes in test set. Four algorithms (GBDT, LASSO, RF, XGBoost) were used for feature selection, and eight algorithms (RF, GBDT, AdaBoost, LR, DT, SVM, NB, KNN) were utilized for classification. (B) Univariate survival analysis of radiomics features. Patients were divided into two groups based on the median value of each feature.

Figure 1. (A) The predictive performance of radiomics features for somatic mutations and molecular subtypes in test set. Four algorithms (GBDT, LASSO, RF, XGBoost) were used for feature selection, and eight algorithms (RF, GBDT, AdaBoost, LR, DT, SVM, NB, KNN) were utilized for classification. (B) Univariate survival analysis of radiomics features. Patients were divided into two groups based on the median value of each feature.