Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma

Purpose To develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation. Materials and Methods One hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination. Results Rad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808-0.940 vs. 0.803; 95% CI: 0.705-0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800-0.921 vs. 0.846; 95% CI: 0.777-0.915, P < 0.05). Conclusions The radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.

Automated summary

The study developed and validated a nomogram combining clinical factors and radiomics features to estimate overall survival in ccRCC patients, showing the incremental value of radiomics for OS estimation. The radiomics nomogram demonstrated higher discrimination capability compared to the clinical nomogram, suggesting its usefulness in assisting quantitative and personalized treatment for ccRCC.