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Background: Recently, the GRANT (GRade, Age, Nodes, and Tumor) score was validated through an adjuvant trial population. Methods: This retrospective study evaluated the performance of the GRANT score as a prognostic model for disease-free survival (DFS), compared to the University of California Los Angeles Integrated Staging System (UISS) score, in a “real-life” population of early renal cell carcinoma patients. A uni-/multivariate analysis of DFS was also performed, to weigh the roles of baseline clinical factors. Results: From February 1998 to January 2018, 134 consecutive patients were enrolled, of which 85 patients (63.4%) had a favorable GRANT score, 49 (36.6%) an unfavorable GRANT score, and 21 (15.7%), 84 (62.6%), and 29 (21.6%) patients had a low, intermediate, or high risk of recurrence according to the UISS score, respectively. The median follow-up was 96 months. The median DFS of the overall study population was 53.7 months (95% CI: 38.4-87.8). Only bilateral renal cell carcinoma (p = 0.0041), Fuhrman grade 3/4 (p = 0.0008), pT3b- 4 (p = 0.0324), and pN1-2 (p = 0.0303) pathological status were confirmed as independent predictors of a shorter DFS by the multivariate analysis. The median DFS of patients with favorable and unfavorable GRANT scores were 84.9 (95% CI: 49.8-129) and 38.4 months (95% CI: 24.4-87.8), respectively, with a statistically significant difference (p = 0.0147). The median DFS of patients with low, intermediate, and high risk of recurrence according to the UISS score were 92.3 (95% CI: 18.1-153.9), 51.7 (95% CI: 36.2-87.8), and 49.8 months (95% CI: 31.3-129), respectively, without statistically significant differences (p = 0.4728). DFS c-statistic values were 0.59 (95% CI: 0.51-0.67) and 0.51 (95% CI: 0.42-0.60) for the GRANT and the UISS scores, respectively. Conclusion: The GRANT score might be a useful tool that is user-friendly and easy to perform in clinical practice.
Current Urology – Karger
Published: Jan 1, 2020
Keywords: GRANT; Renal cell carcinoma; Disease-free survival; Kidney; Predictive model
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