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J Urol. 2002 Dec;168(6):2395-400.

An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score.

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1
Department of Urology, Mayo Medical School and Mayo Clinic, Rochester, Minnesota, USA.

Abstract

PURPOSE:

Currently outcome prediction in renal cell carcinoma is largely based on pathological stage and tumor grade. We developed an outcome prediction model for patients treated with radical nephrectomy for clear cell renal cell carcinoma, which was based on all available clinical and pathological features significantly associated with death from renal cell carcinoma.

MATERIALS AND METHODS:

We identified 1,801 adult patients with unilateral clear cell renal cell carcinoma treated with radical nephrectomy between 1970 and 1998. Clinical features examined included age, sex, smoking history, and signs and symptoms at presentation. Pathological features examined included 1997 TNM stage, tumor size, nuclear grade, histological tumor necrosis, sarcomatoid component, cystic architecture, multifocality and surgical margin status. Cancer specific survival was estimated using the Kaplan-Meier method. Cox proportional hazards regression models were used to test associations between features studied and outcome. The selection of features included in the multivariate model was validated using bootstrap methodology.

RESULTS:

Mean followup was 9.7 years (range 0.1 to 31). Estimated cancer specific survival rates at 1, 3, 5, 7 and 10 years were 86.6%, 74.0%, 68.7%, 63.8% and 60.0%, respectively. Several features were multivariately associated with death from clear cell renal cell carcinoma, including 1997 TNM stage (p <0.001), tumor size 5 cm. or greater (p <0.001), nuclear grade (p <0.001) and histological tumor necrosis (p <0.001).

CONCLUSIONS:

In patients with clear cell renal cell carcinoma 1997 TNM stage, tumor size, nuclear grade and histological tumor necrosis were significantly associated with cancer specific survival. We present a scoring system based on these features that can be used to predict outcome.

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