{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:06:25Z","timestamp":1789707985168,"version":"4.0.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Urol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 1210 extracted features (after excluding general_info fields), 851 showed good to excellent reproducibility (ICC &amp;gt; 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\u00a0=\u00a00.793, 95% CI: 0.72-0.85; DeLong test, P\u00a0=\u00a00.015). 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fruro.2026.1928982","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:36:19Z","timestamp":1789706179000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma"],"prefix":"10.3389","volume":"6","author":[{"given":"Fa-de","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Urology, Jiangning Clinical Medical College of Kangda College of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]},{"name":"Department of Urology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-li","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangning Clinical Medical College of Kangda College of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]},{"name":"Department of Urology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Du-jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangning Clinical Medical College of Kangda College of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]},{"name":"Department of Urology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-bing","family":"Ju","sequence":"additional","affiliation":[{"name":"Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Urology, Jiangning Clinical Medical College of Kangda College of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]},{"name":"Department of Urology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing","place":["Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1016\/s0140-6736(15)00046-x","article-title":"Renal cancer","volume":"387","author":"Capitanio","year":"2016","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2017.02.015","article-title":"Exploratory subgroup analyses of renal function and overall survival in European Organization for Research and Treatment of Cancer randomized trial of nephron-sparing surgery versus radical nephrectomy","volume":"3","author":"Scosyrev","year":"2017","journal-title":"Eur Urol Focus"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2025.02.020","article-title":"European Association of Urology guidelines on renal cell carcinoma: The 2025 update","volume":"87","author":"Bex","year":"2025","journal-title":"Eur Urol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ucl.2016.12.004","article-title":"Renal tumor anatomic complexity: Clinical implications for urologists","volume":"44","author":"Joshi","year":"2017","journal-title":"Urol Clin North Am"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2009.05.035","article-title":"The R.E.N.A.L. nephrometry score: A comprehensive standardized system for quantitating renal tumor size, location and depth","volume":"182","author":"Kutikov","year":"2009","journal-title":"J Urol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ucl.2023.06.007","article-title":"Radiomics and artificial intelligence: Renal cell carcinoma","volume":"51","author":"Raman","year":"2024","journal-title":"Urol Clin North Am"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-017-3849-5","article-title":"Pathologic analysis of non-neoplastic parenchyma in renal cell carcinoma: A comprehensive observation in radical nephrectomy specimens","volume":"17","author":"Wang","year":"2017","journal-title":"BMC Cancer"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2016.05.084","article-title":"Re: Percutaneous kidney biopsy for a small renal mass: A critical appraisal of results","volume":"196","author":"Bhattar","year":"2016","journal-title":"J Urol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2015.09.073","article-title":"Percutaneous kidney biopsy for a small renal mass: A critical appraisal of results","volume":"195","author":"Jeon","year":"2016","journal-title":"J Urol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2009.09.095","article-title":"Percutaneous biopsy of renal cell carcinoma underestimates nuclear grade","volume":"76","author":"Blumenfeld","year":"2010","journal-title":"Urology"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-017-1144-1","article-title":"Texture analysis as a radiomic marker for differentiating renal tumors","volume":"42","author":"Yu","year":"2017","journal-title":"Abdom Radiol (NY)"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13061348","article-title":"Radiomics in renal cell carcinoma\u2013A systematic review and meta-analysis","volume":"13","author":"M\u00fchlbauer","year":"2021","journal-title":"Cancers (Basel)"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-019-0195-7","article-title":"CT texture analysis: A potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma","volume":"19","author":"Feng","year":"2019","journal-title":"Cancer Imaging"},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.62347\/GUWV5636","article-title":"Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis","volume":"12","author":"Dong","year":"2024","journal-title":"Am J Clin Exp Urol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-019-01992-7","article-title":"CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma","volume":"44","author":"Lin","year":"2019","journal-title":"Abdom Radiol (NY)"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1821876","article-title":"Imaging tool for predicting renal clear cell carcinoma Fuhrman grade: Comparing R.E.N.A.L. nephrometry score and CT texture analysis","volume":"2021","author":"Sun","year":"2021","journal-title":"Biomed Res Int"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2014.09.007","article-title":"Renal cell carcinoma subtype differentiation using single-phase corticomedullary contrast-enhanced CT","volume":"39","author":"Veloso Gomes","year":"2015","journal-title":"Clin Imaging"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2015.04.004","article-title":"Angiomyolipoma with minimal fat: Differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images","volume":"22","author":"Yan","year":"2015","journal-title":"Acad Radiol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.18.20742","article-title":"Unenhanced CT texture analysis of clear cell renal cell carcinomas: A machine learning\u2013based study for predicting histopathologic nuclear grade","volume":"212","author":"Kocak","year":"2019","journal-title":"AJR Am J Roentgenol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J Chiropr Med"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc Ser B"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1177\/0272989x06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"B25","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1097\/01.ju.0000141137.61330.4d","article-title":"Symptoms as well as tumor size provide prognostic information on patients with localized renal tumors","volume":"172","author":"Patard","year":"2004","journal-title":"J Urol"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.15.15451","article-title":"CT textural analysis of large primary renal cell carcinomas: Pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes","volume":"207","author":"Lubner","year":"2016","journal-title":"AJR Am J Roentgenol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1102\/1470-7330.2013.0015","article-title":"Quantifying tumour heterogeneity with CT","volume":"13","author":"Ganeshan","year":"2013","journal-title":"Cancer Imaging"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa1113205","article-title":"Intratumor heterogeneity and branched evolution revealed by multiregion sequencing","volume":"366","author":"Gerlinger","year":"2012","journal-title":"N Engl J Med"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbcan.2009.11.002","article-title":"Tumor heterogeneity: Causes and consequences","volume":"1805","author":"Marusyk","year":"2010","journal-title":"Biochim Biophys Acta"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2018.04.013","article-title":"CT-based radiomic model predicts high grade of clear cell renal cell carcinoma","volume":"103","author":"Ding","year":"2018","journal-title":"Eur J Radiol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5698-2","article-title":"Clear cell renal cell carcinoma: Machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade","volume":"29","author":"Bektas","year":"2019","journal-title":"Eur Radiol"}],"container-title":["Frontiers in Urology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fruro.2026.1928982\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:36:26Z","timestamp":1789706186000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fruro.2026.1928982\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":32,"alternative-id":["10.3389\/fruro.2026.1928982"],"URL":"https:\/\/doi.org\/10.3389\/fruro.2026.1928982","relation":{},"ISSN":["2673-9828"],"issn-type":[{"value":"2673-9828","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]},"article-number":"1928982"}}