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A review of integrated staging systems for renal cell carcinoma
Authors:Meskawi Malek  Sun Maxine  Trinh Quoc-Dien  Bianchi Marco  Hansen Jens  Tian Zhe  Rink Michael  Ismail Salima  Shariat Shahrokh F  Montorsi Francesco  Perrotte Paul  Karakiewicz Pierre I
Institution:Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Canada.
Abstract:

Context

Several outstanding integrated staging systems (ISSs) have been devised for patients with renal cell carcinoma (RCC).

Objective

To review the available literature on existing ISSs.

Evidence acquisition

A nonsystematic search was conducted using Medline and PubMed databases. Original articles, review articles, and editorials addressing the development and validation of ISSs in RCC published up to February 2012 were identified. The search was limited to the English language. Keywords included kidney cancer, renal cell carcinoma, nomogram, risk group, prognosis, predictive accuracy, external validation, and discrimination. Links to related articles and cross-reading of citations in related articles were surveyed. All articles with a pertinent level of evidence were included and represent the basis for the current review article.

Evidence synthesis

In nephrectomy patients, a variety of models have been developed for prediction of recurrence and survival, both in the preoperative and postoperative settings. Several of those models relied on variables that are not routinely available in clinical practice. Not all tools were externally validated. In patients treated with systemic therapy, novel tools that were developed and validated in the targeted therapy era replaced tools devised during the cytokine era.

Conclusions

The development of ISSs for prediction of risk or prognosis in the context of RCC has evolved and improved. In the targeted therapy era, the urologic community should focus on direct comparisons of existing tools with the intent of identifying the optimal ISS for each specific end point.
Keywords:Integrated staging system  Renal cell carcinoma  Prognostic models
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