Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation - Jiang, Yi; Li, Wuchao; Huang, Chencui; Tian, Chong; Chen, Qi; Zeng, Xianchun; Cao, Yin; Chen, Yi; Yang, Yintong; Liu, Heng; Bo, Yonghua; Luo, Chenggong; et al. (Frontiers in Oncology, 2020)
Objective: To develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with clear cell renal cell carcinoma (ccRCC) in multicenters. Methods: In total, 430 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (score 0-3) and intermediate to high-risk group (score ≥4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups were evaluated with regard to their discrimination, calibration, and clinical usefulness. Results: An radiomic signature consisting of eleven relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer–Lemeshow p＞0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884–0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.805–0.929; AUC: 0.928, 95% CI: 0.844–0.975; respectively). The radiomic signature performed better than the image feature model constructed by the intratumoral vessels (all p < 0.05) and showed a similar performance with the fusion model integrating radiomic signature and intratumoral vessels (all p＞0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts. Conclusion: Radiomic signature could be used as a promising noninvasive tool to predict SSIGN risk groups and to facilitate preoperative clinical decision-making for patients with ccRCC.