Development and External Validation of Nomograms for Predicting Disease-Free Survival and Overall Survival in Patients with cT1-ccRCC After Partial Nephrectomy: A Multicenter Retrospective Study

• Published in: Annals of surgical oncology 2024-09, Vol.31 (9), p.5827-5838
• Main Authors: Xu, Haozhe, Xing, Zhuo, Wang, Jie, Lv, Zhengtong, Deng, Piye, Hong, Yulong, Li, Yuan
• Format: Article
• Language: English
• Subjects: Clear cell-type renal cell carcinoma; Medicine; Medicine & Public Health; Nephrectomy; Nomograms; Oncology; Surgery; Surgical Oncology; Urologic Oncology
• ISSN: 1068-9265; 1534-4681; 1534-4681
• DOI: 10.1245/s10434-024-15718-7

Background To develop a novel nomogram for predicting 2-year and 5-year disease-free survival (DFS) and overall survival (OS) in patients with cT1-clear cell renal cell carcinoma (ccRCC) undergoing partial nephrectomy (PN). Methods A retrospective study was conducted across five urological centers, including 940 patients who underwent PN for cT1N0M0-ccRCC. Four centers were randomly selected to constitute the training group, while the remaining center served as the testing group. We employed the LASSO and multivariate Cox regression to develop new nomograms. The 1,000 bootstrap-corrected c-index, net reclassification improvement (NRI) and receiver operating characteristic curve were employed to compare the predictive abilities of new nomograms with the widely used UUIS and SSIGN models. Finally, the novel nomograms underwent external validation. Results The training group included 714 patients, while the testing group consisted of 226 patients. The bootstrap-corrected c-indexes for the DFS and OS model were 0.870 and 0.902, respectively. In the training cohort, the AUC for the DFS and OS models at 2 years and 5 years were 0.953, 0.902, 0.988, and 0.911, respectively. These values were also assessed in the testing cohort. The predictive capabilities of the new nomograms surpassed those of the UUIS and SSIGN models (NRI > 0). Decision curve analysis demonstrated that the novel nomograms provide greater net benefits compared to the UUIS and SSIGN models. Conclusions Our novel nomograms demonstrated strong predictive ability for forecasting oncological outcomes in cT1-ccRCC patients after PN. These user-friendly nomograms are simple and convenient for clinical application, providing tangible clinical benefits.