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Renal Cancer pp 345-359 | Cite as

Outcomes: Prognostic Factors, Models, and Algorithms

  • Kristian D. StenslandEmail author
  • Michael W. Kattan
Chapter

Abstract

Renal cell carcinoma has a wide and varied clinical presentation and natural history, and this heterogeneity can be problematic when it comes to providing the individualized outcome predictions that contemporary patients crave. This heterogeneity, together with the cost and toxicity of both systemic and local therapies, underscores the need for prediction models and algorithms that can help to identify which patients will experience the most amount of therapeutic benefit and incur the least amount of treatment-related harm. Tumor stage and cellular characteristics were once considered the primary determinants of overall prognosis but have now become components of more refined clinical algorithms and nomograms that incorporate clinical, pathologic, and molecular data. These prediction tools have the capability to provide individualized risk estimations in an unbiased, reproducible, and evidence-based format, and currently, models have been constructed and validated in the preoperative, postoperative, and metastatic settings for RCC. As our understanding of the implications of molecular markers continues to develop, the incorporation of these variables into existing models should improve not only our selection of systemic therapies and clinical trials but also patient satisfaction and outcomes.

Keywords

Algorithm Nomogram Outcomes Prediction tools Renal cancer 

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Division of UrologyLahey Hospital and Medical CenterBurlingtonUSA
  2. 2.Department of Quantitative Health SciencesCleveland ClinicClevelandUSA

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