Research Paper Volume 13, Issue 7 pp 9960—9975

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

The flowchart of radiomics analysis and omics integration. (1) Manual delineation of tumor region of interest (ROI) of contrast-enhanced CT. Shape-based, first-order and second-order radiomics features of ROIs were then calculated. (2) Prediction of somatic mutations and molecular subtypes using radiomics features and multiple machine learning algorithms in independent training/test sets. (3) Radiomics, genomics, transcriptomics and proteomics were integrated to build predictive models for overall survival in training set, and their prognostic values were estimated using validation set.

Figure 6. The flowchart of radiomics analysis and omics integration. (1) Manual delineation of tumor region of interest (ROI) of contrast-enhanced CT. Shape-based, first-order and second-order radiomics features of ROIs were then calculated. (2) Prediction of somatic mutations and molecular subtypes using radiomics features and multiple machine learning algorithms in independent training/test sets. (3) Radiomics, genomics, transcriptomics and proteomics were integrated to build predictive models for overall survival in training set, and their prognostic values were estimated using validation set.