The R.E.N.A.L. nephrometric nomogram cannot accurately predict malignancy or aggressiveness of small renal masses amenable to partial nephrectomy

Alessandro Antonelli, Maria Furlan, Marco Sandri, Andrea Minervini, Luca Cindolo, Paolo Parma, Stefano Zaramella, Angelo Porreca, Gianni Vittori, Alessandro Samuelli, Donato Dente, Francesco Berardinelli, Maria Rosaria Raspollini, Sergio Serni, Marco Carini, Carlo Terrone, Luigi Schips, Claudio Simeone

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

The R.E.N.A.L. nephrometry nomogram (RNN) is a statistical tool to predict the malignancy or aggressiveness of renal masses. In this study, we tried an external validation in a cohort of 506 cT1a tumors submitted to partial nephrectomy (PN), showing a poor predictive ability. We can conclude that this nomogram cannot contribute to the preoperative management of small renal masses (SRM). Introduction/Background: The prediction of histology of SRM could be essential for their management. The RNN is a statistical tool designed to predict malignancy or high grading of enhancing renal masses. In this study we aimed to perform an external validation of the RNN in a cohort of patients who received a PN for SRM. Materials and Methods: This was a multicentric study in which the data of 506 consecutive patients who received a PN for cT1a SRM between January 2010 and January 2013 were analyzed. For each patient, the probabilities of malignancy and aggressiveness were estimated preoperatively using the RNN. The performance of the RNN was evaluated according to receiver operating characteristic (ROC) curve, calibration plot, and decision curve analyses. Results: The area under the ROC curve for malignancy was 0.57 (95% confidence interval [CI], 0.51-0.63; P = .031). The calibration plot showed that the predicted probability of malignancy had a bad concordance with observed frequency (Brier score = 0.17; 95% CI, 0.15-0.19). Decision curve analysis confirmed a poor clinical benefit fromuse of the system. The estimated area under the ROC curve for high-grade prediction was 0.57 (95% CI, 0.49-0.66; P = .064). The calibration plot evidenced a bad concordance (Brier score = 0.15; 95% CI, 0.13-0.17). Decision curve analysis showed the lack of a remarkable clinical usefulness of the RNN when predicting aggressiveness. Conclusions: The RNN cannot accurately predict histology in the setting of cT1a SRM amenable to PN.

Lingua originaleInglese
pagine (da-a)366-372
Numero di pagine7
RivistaClinical Genitourinary Cancer
Volume12
Numero di pubblicazione5
DOI
Stato di pubblicazionePubblicato - 2014
Pubblicato esternamente

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