CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma. Issue 5 (4th September 2021)

• Record Type: Journal Article
• Authors: Wen, Rong; Huang, Jing; Gao, Rui-zhi; Wan, Da; Qin, Hui; Peng, Yu-ting; Liang, Yi-qiong; Li, Xin; Wang, Xin-rong; He, Yun; Yang, Hong
• Is Part Of: Journal of computer assisted tomography. Volume 45:Issue 5(2021)
• Page Start: 696
• Page End: 703
• Publication Date: 2021-09-04
• Subjects: multidetector computed tomography -- radiomics -- clear cell renal cell carcinoma -- distant metastasis; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001211
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Supplemental digital content is available in the text. Abstract : Purpose: The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients. Methods: Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. Results: The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81–0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69–0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. Conclusions: The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.