COMPARATIVE STUDY
JOURNAL ARTICLE

The 'Stage, Size, Grade and Necrosis' score is more accurate than the University of California Los Angeles Integrated Staging System for predicting cancer-specific survival in patients with clear cell renal cell carcinoma

Vincenzo Ficarra, Giacomo Novara, Antonio Galfano, Matteo Brunelli, Stefano Cavalleri, Guido Martignoni, Walter Artibani
BJU International 2009, 103 (2): 165-70
18782313

OBJECTIVE: To compare the prognostic accuracy of the two most used integrated staging systems to predict the outcome of patients with clear cell renal cell carcinoma (RCC).

PATIENTS AND METHODS: We retrospectively evaluated the clinical and pathological data of 388 patients surgically treated for clear cell RCC between 1986 and 2000. The pathological slides of all specimens were reviewed by a pathologist unaware of patient outcome. All patients were classified according to the 'Stage, Size, Grade and Necrosis' (SSIGN) score and University of California Los Angeles Integrated Staging System (UISS) model, and the predictive accuracy of the two models was evaluated using receiver operating characteristics (ROC) curves.

RESULTS: The median follow-up was 56 months; the 10-year cancer-specific survival (CSS) probabilities according to the SSIGN score were 96% in the '0-2' category, 78% in the '3-4', 43% in the '5-6', 25.8% in the '7-9' and 0% in the '> or =10' group (P < 0.001). According to the UISS, in nonmetastatic patients the 10-year CSS probabilities were 100% in low, 73% in intermediate and 62% in high-risk groups; in metastatic patients the respective CSS probabilities were 37%, 33% and 12.5% (P < 0.001). The area under the ROC curve (AUC) was 0.870 for the SSIGN score and 0.832 for the UISS. Including only nonmetastatic patients in the analysis, the AUC was 0.830 for the SSIGN score and 0.760 for the UISS model.

CONCLUSION: Our study shows for the first time that the SSIGN score offers a better stratification of clear cell RCC than the UISS model. These data should be considered in the design of future randomized controlled trials.

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