{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:03:25Z","timestamp":1789985005930,"version":"4.0.1"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian Provincial Natural Science Foundation of China","award":["2026D016"],"award-info":[{"award-number":["2026D016"]}]},{"name":"Fujian Provincial Natural Science Foundation of China","award":["2026J0011897"],"award-info":[{"award-number":["2026J0011897"]}]},{"name":"Xiamen Health High-Level Development Science and Technology Program","award":["2024GZL-QN027"],"award-info":[{"award-number":["2024GZL-QN027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Accurate preoperative discrimination between indolent and aggressive renal lesions is essential for personalized risk-stratified management and improves the clinical outcomes of renal tumor patients. This study aimed to establish and externally validate a non-invasive computed tomography (CT)-derived radiomic model for identifying aggressive renal tumors and predicting long-term postoperative survival. A total of 3407 patients with pathologically confirmed renal tumors from multiple centers were retrospectively enrolled and divided into three independent cohorts: a training cohort (n = 1840), an internal validation cohort (n = 789), and an external test cohort (n = 778). Based on preoperative multiphasic CT images, a support vector machine (SVM)-based radiomic model was constructed to evaluate tumor aggressiveness. Cox proportional hazards regression analysis was performed to identify independent prognostic factors, and a comprehensive nomogram combining radiomic aggressiveness score and clinicopathological parameters was further developed for survival prediction. The established SVM radiomic model yielded stable discriminatory performance, with an area under the receiver operating characteristic curve (AUC) of 0.740 in the internal validation cohort and 0.763 in the external test cohort. Multivariate Cox regression analysis demonstrated that radiomics-predicted tumor aggressiveness remained an independent adverse prognostic factor for recurrence-free survival (HR = 3.656), overall survival (HR = 1.981), and disease-specific survival (HR = 3.673). Moreover, the integrated nomogram demonstrated favorable calibration for predicting 5-year and 8-year overall survival in the internal validation cohort. This novel CT-based radiomic model provides a robust non-invasive tool for preoperative differentiation of renal tumor aggressiveness. As an independent prognostic biomarker, it can assist clinicians in precise preoperative risk evaluation and facilitate individualized therapeutic decision-making for renal tumor patients.<\/jats:p>","DOI":"10.3390\/bioengineering13091084","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T08:03:47Z","timestamp":1789977827000},"page":"1084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noninvasive Preoperative Stratification of Renal Tumors for Aggressiveness and Prognosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3310-601X","authenticated-orcid":false,"given":"Jinglai","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China"},{"name":"Xiamen Clinical Research Center for Cancer Therapy, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China"},{"name":"Clinical Research Center for Precision Medicine of Abdominal Tumor of Fujian Province, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China"},{"name":"Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Linpeng","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, 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