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Predictive models for the practical management of renal cell carcinoma

Nature Reviews Urology volume 9, pages 73–84 (2012)Cite this article

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Abstract

The expanding availability of multiple therapeutic strategies and sequencing options for patients with renal cell carcinoma (RCC) has increased the importance of skilled individualized outcome estimation for patients. This need has driven the development of statistical models to guide patient management in a variety of common clinical settings, including the management of small renal masses, identification of patients with high-risk localized RCC requiring systemic therapy and selection of suitable targeted therapies in metastatic disease. With an increasing number of different predictive models described in the literature, identifying those models most relevant for practical use is challenging. In addition to statistical models based on clinical data, there has also been an evolution towards incorporation of molecular markers into predictive algorithms. These models also serve as important benchmarks for the researchers developing novel prognostic and predictive molecular biomarkers.

Key Points

  • Although many models for renal cell carcinoma (RCC) have been developed to guide clinical decision making in common clinical scenarios, few have been externally validated for practical use

  • Patients with incidentally detected small renal masses should have individual competing risks estimated for noncancer death alongside the risk of malignancy for the purpose of preoperative counseling

  • Nomograms, such as the Karakiewicz nomogram, provide the most useful individual predictions for estimations of postnephrectomy survival for postoperative counseling and surveillance

  • Prognostic clinical models for metastatic RCC have been integrated into patient selection for targeted therapy clinical trials and should be used to guide recommendations for appropriate first-line treatment, including cytoreductive nephrectomy and systemic therapy

  • Research aimed at using molecular biomarkers to improve outcome estimation in RCC should use the best existing clinical models as benchmarks to ensure utility

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Figure 1: Photomicrographs of human RCC tissue (hematoxylin and eosin staining).
Figure 2: A flow chart of the natural history of RCC.

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Acknowledgements

We would like to thank Dr Puay Hoon Tan (Department of Pathology, Singapore General Hospital, Singapore) for kindly providing the histological images.

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  1. Department of Urology, Singapore General Hospital, Outram Road, 169608, Singapore, Republic of Singapore

    Lui Shiong Lee

  2. Institute of Bioengineering and Nanotechnology, 31 Biopolis Way, The Nanos, #04-01, 138669, Singapore, Republic of Singapore

    Min-Han Tan

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L. S. Lee and M.-H. Tan contributed equally to the research for the article and discussions of content as well as the writing and editing of this manuscript before submission.

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Correspondence to Min-Han Tan.

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Lee, L., Tan, MH. Predictive models for the practical management of renal cell carcinoma. Nat Rev Urol 9, 73–84 (2012). https://doi.org/10.1038/nrurol.2011.224

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