Frontiers in urology · 2026
Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma.
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Liu Fa-de, Yu Hui-Li, Wang Du-Jian, Ju Xiao-Bing, Yang Yan-Hao
Аннотация
OBJECTIVES: To develop a CT-based radiomic model for predicting partial nephrectomy (PN) in clear cell renal cell carcinoma (ccRCC) and compare it with the R.E.N.A.L. Nephrometry Score (RNS).
METHODS: This retrospective study included 413 ccRCC patients who underwent PN or radical nephrectomy. The RNS was calculated from preoperative CT. Radiomic features were extracted from corticomedullary-phase CT. Reproducibility was assessed using intraclass correlation coefficients (ICCs). LASSO regression was used for feature selection and Rad-score construction. Multivariable logistic regression models were built based on the total RNS sum and the Rad-score. Performance was evaluated by AUC, calibration, and decision curve analysis.
RESULTS: Of 1210 extracted features (after excluding general_info fields), 851 showed good to excellent reproducibility (ICC > 0.75). The radiomic model (6 selected features) achieved an AUC of 0.886 (95% CI: 0.81-0.91) in the internal validation cohort, significantly outperforming the RNS model (AUC = 0.793, 95% CI: 0.72-0.85; DeLong test, P = 0.015). The radiomic model also demonstrated better calibration (Brier score: 0.11 vs. 0.16) and higher clinical net benefit. SHAP analysis identified tumor size and texture heterogeneity as the strongest predictors.
CONCLUSION: A CT-based radiomic model provides superior accuracy over the RNS for preoperatively predicting PN likelihood in ccRCC and holds significant potential to augment surgical decision-making and personalize treatment planning. Prospective multi-center validation is warranted before routine clinical adoption.
METHODS: This retrospective study included 413 ccRCC patients who underwent PN or radical nephrectomy. The RNS was calculated from preoperative CT. Radiomic features were extracted from corticomedullary-phase CT. Reproducibility was assessed using intraclass correlation coefficients (ICCs). LASSO regression was used for feature selection and Rad-score construction. Multivariable logistic regression models were built based on the total RNS sum and the Rad-score. Performance was evaluated by AUC, calibration, and decision curve analysis.
RESULTS: Of 1210 extracted features (after excluding general_info fields), 851 showed good to excellent reproducibility (ICC > 0.75). The radiomic model (6 selected features) achieved an AUC of 0.886 (95% CI: 0.81-0.91) in the internal validation cohort, significantly outperforming the RNS model (AUC = 0.793, 95% CI: 0.72-0.85; DeLong test, P = 0.015). The radiomic model also demonstrated better calibration (Brier score: 0.11 vs. 0.16) and higher clinical net benefit. SHAP analysis identified tumor size and texture heterogeneity as the strongest predictors.
CONCLUSION: A CT-based radiomic model provides superior accuracy over the RNS for preoperatively predicting PN likelihood in ccRCC and holds significant potential to augment surgical decision-making and personalize treatment planning. Prospective multi-center validation is warranted before routine clinical adoption.