AUTHOR=Yan Lei , Yang Guangjie , Cui Jingjing , Miao Wenjie , Wang Yangyang , Zhao Yujun , Wang Ning , Gong Aidi , Guo Na , Nie Pei , Wang Zhenguang 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.671420

DOI=10.3389/fonc.2021.671420

ISSN=2234-943X

ABSTRACT=Purpose: This study was designed to develop and validate the radiomics nomogram combined clinical factors and radiomics 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: 194 ccRCC cases were included as the training cohort and 188 ccRCC cases from another hospital were configured as the test cohort. Three-dimensional region-of-interest segmentation was segmented manually 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. Association between the Rad-score and OS was explored. The radiomics nomogram combined clinical factors and Rad-score was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evalated with respect to calibration and discrimination.
Results: The Rad-score calculated via 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 than the 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 demonstrates good performance for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.