{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:36:47Z","timestamp":1787027807376,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Accurate prognostic stratification after nephrectomy for clear cell renal carcinoma (ccRCC) remains challenging. Traditional models often lack the intuitive clinical application or the ability to handle non-linear interactions between variables. We aimed to develop and internally validate a decision tree-based model to predict cancer-specific survival in patients with ccRCC following nephrectomy. Methods: We analyzed 79,526 patients with ccRCC who underwent nephrectomy from the SEER database (2012\u20132018). Patients were randomized into development (2\/3) and validation (1\/3) cohorts. A conditional inference tree was constructed to predict cancer-specific survival. Multiple imputation by chained equations was used to handle missing data. Discriminatory ability was assessed using the C-index. Net clinical benefit was evaluated with decision curve analysis. The model was evaluated using CHARMS and PROBAST. Results: A decision tree with 15 risk groups is presented, further classified into high-, intermediate-, and low-risk categories according to observed median survival. The final predictors were tumor localization, tumor grade, TNM stage, age, and sarcomatoid differentiation. The model demonstrated excellent discriminatory performance, with a C-index of 0.846 (95% CI: 0.834\u20130.847). PROBAST assessment showed low risk of bias and low concern regarding applicability. Conclusions: The use of decision trees provides an interpretable alternative to conventional regression-based models. Three main risk categories and 15 subgroups are proposed based on tumor localization, tumor grade, TNM stage, age, and sarcomatoid differentiation. Our model demonstrates good applicability and a low risk of bias according to PROBAST guidelines; however, external validation in independent cohorts is required prior to clinical implementation.<\/jats:p>","DOI":"10.3390\/cancers18162644","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T10:04:36Z","timestamp":1786961076000},"page":"2644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Decision Tree to Predict Cancer-Specific Mortality in Patients with Clear Cell Renal Cancer Treated with Nephrectomy"],"prefix":"10.3390","volume":"18","author":[{"given":"Laura","family":"Mart\u00ednez-Cayuelas","sequence":"first","affiliation":[{"name":"Urology Services, University Hospital of San Juan de Alicante, 03550 San Juan de Alicante, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8654-4433","authenticated-orcid":false,"given":"Pau","family":"Sarrio-Sanz","sequence":"additional","affiliation":[{"name":"Urology Services, University Hospital of San Juan de Alicante, 03550 San Juan de Alicante, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0884-0472","authenticated-orcid":false,"given":"Jose-Vicente","family":"Segura-Heras","sequence":"additional","affiliation":[{"name":"Instituto Centro de Investigaci\u00f3n Operativa, Miguel Hern\u00e1ndez University, 03202 Elche, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milagros","family":"Mu\u00f1oz-Montoya","sequence":"additional","affiliation":[{"name":"Urology Services, University Hospital of San Juan de Alicante, 03550 San Juan de Alicante, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3793-9786","authenticated-orcid":false,"given":"Vicente-Francisco","family":"Gil-Guillen","sequence":"additional","affiliation":[{"name":"Clinical Medicine Department, Miguel Hern\u00e1ndez University, 03550 San Juan de Alicante, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesus","family":"Romero-Maroto","sequence":"additional","affiliation":[{"name":"Pathology and Surgery Department, Miguel Hern\u00e1ndez University, 03550 San Juan de Alicante, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1396-0484","authenticated-orcid":false,"given":"Luis","family":"Gomez-Perez","sequence":"additional","affiliation":[{"name":"Pathology and Surgery Department, Miguel Hern\u00e1ndez University, 03550 San Juan de Alicante, Alicante, Spain"},{"name":"Urology Services, University and General Hospital of Elche, 03203 Elche, Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhanghuang, C., Wang, J., Zhang, Z., Jin, L., Tan, X., Mi, T., Liu, J., Li, M., and He, D. 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