PURPOSE: To confirm predictive accuracies of the RENAL nephrometry score (RNS) nomogram for identifying malignancy and high-grade renal cell carcinoma (RCC) in an external cohort of small renal masses (SRMs). METHODS: A total of 1,129 patients who underwent extirpative renal surgery for solid and enhancing cT1 renal tumors between 2005 and 2012 at a single institution were included in the validation cohort. A single uro-radiologist utilized computed tomography image reconstruction to classify tumors according to the RNS. The area under the curve (AUC) and calibration plots were used to determine predictive accuracies of malignancy and high-grade models of the RNS nomogram. RESULTS: Malignant and high-grade tumors were identified in 1,012 (89.6%) and 389 (38.4%) patients with cT1 tumors, and in 658 (87.3%) and 215 (32.6%) patients with cT1a tumors, respectively. Predictive performances of the nomogram for malignancy and high-grade models revealed AUCs of 0.722 and 0.574 for cT1 tumors, and 0.727 and 0.495 for cT1a tumors, respectively. The predictive value of the malignancy model was comparable to that of the model-development cohort (AUC = 0.76); however, the predictive value of the high-grade model was inferior to that of the model-development cohort (AUC = 0.73). CONCLUSIONS: Unlike previous validation studies, we report inferior predictive performance of the RNS nomogram for discriminating high-grade RCC in solid and enhancing SRMs. This suggests that the RNS nomogram may be unreliable for preoperatively predicting high-grade RCC in SRMs, in which tumor size, the key determinant of high-grade RCC, is a limiting factor.
PURPOSE: To confirm predictive accuracies of the RENAL nephrometry score (RNS) nomogram for identifying malignancy and high-grade renal cell carcinoma (RCC) in an external cohort of small renal masses (SRMs). METHODS: A total of 1,129 patients who underwent extirpative renal surgery for solid and enhancing cT1renal tumors between 2005 and 2012 at a single institution were included in the validation cohort. A single uro-radiologist utilized computed tomography image reconstruction to classify tumors according to the RNS. The area under the curve (AUC) and calibration plots were used to determine predictive accuracies of malignancy and high-grade models of the RNS nomogram. RESULTS: Malignant and high-grade tumors were identified in 1,012 (89.6%) and 389 (38.4%) patients with cT1tumors, and in 658 (87.3%) and 215 (32.6%) patients with cT1a tumors, respectively. Predictive performances of the nomogram for malignancy and high-grade models revealed AUCs of 0.722 and 0.574 for cT1tumors, and 0.727 and 0.495 for cT1a tumors, respectively. The predictive value of the malignancy model was comparable to that of the model-development cohort (AUC = 0.76); however, the predictive value of the high-grade model was inferior to that of the model-development cohort (AUC = 0.73). CONCLUSIONS: Unlike previous validation studies, we report inferior predictive performance of the RNS nomogram for discriminating high-grade RCC in solid and enhancing SRMs. This suggests that the RNS nomogram may be unreliable for preoperatively predicting high-grade RCC in SRMs, in which tumor size, the key determinant of high-grade RCC, is a limiting factor.
Authors: Claudio Jeldres; Maxine Sun; Daniel Liberman; Giovanni Lughezzani; Alexandre de la Taille; Jacques Tostain; Antoine Valeri; Luca Cindolo; Vincenzo Ficarra; Walter Artibani; Richard Zigeuner; Arnaud Mejean; Jean Luc Descotes; Eric Lechevallier; Peter F Mulders; Paul Perrotte; Jean-Jacques Patard; Pierre I Karakiewicz Journal: J Urol Date: 2009-12 Impact factor: 7.450
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