JOURNAL ARTICLE

Development and external validation of a new outcome prediction model for patients with clear cell renal cell carcinoma treated with nephrectomy based on preoperative serum C-reactive protein and TNM classification: the TNM-C score

Yasumasa Iimura, Kazutaka Saito, Yasuhisa Fujii, Jiro Kumagai, Satoru Kawakami, Yoshinobu Komai, Junji Yonese, Iwao Fukui, Kazunori Kihara
Journal of Urology 2009, 181 (3): 1004-12; discussion 1012
19150550

PURPOSE: C-reactive protein has been shown to be a prognostic factor for renal cell carcinoma. We developed a new prediction model including C-reactive protein in patients with clear cell renal cell carcinoma.

MATERIALS AND METHODS: This study is based on 2 cohorts of Japanese patients with clear cell renal cell carcinoma, including 249 for evaluating prognostic factors and developing the prediction model, and 290 for external validation. Analyzed factors included TNM classification, tumor size, Fuhrman nuclear grade, tumor necrosis and preoperative serum C-reactive protein (cutoff 0.5 mg/dl). We developed a scoring model based on multivariate analysis to predict cancer specific survival. Predictive ability of the model was evaluated using the concordance index.

RESULTS: Multivariate analysis showed that pT stage, lymph node involvement, distant metastasis, tumor necrosis and C-reactive protein were independent predictors of cancer specific survival. A new scoring model was developed, consisting of C-reactive protein and the TNM classification. The 5-year cancer specific survival rate in patients with a score of 0, 1 and 2, 3 and 4, and 5 or more was 99%, 89%, 69% and 18%, respectively. Cancer specific survival rates were clearly discriminated by the stratification according to the scoring model (p <0.001). The concordance index of the new model was 0.820, which was externally validated as a concordance index of 0.865.

CONCLUSIONS: In patients with clear cell renal cell carcinoma a new simple scoring model based on serum C-reactive protein and the TNM classification is a useful and easy to use tool for predicting outcome.

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