Beyond The Abstract Mayo Clinic Validation Of The D'Amico Risk Group Classification For Predicting Survival Following Radical Prostatectomy

Main Category: Prostate / Prostate Cancer
Also Included In: Urology / Nephrology;  Men's health
Article Date: 16 Mar 2008 - 0:00 PDT

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UroToday.com - The study here externally validates the ability of the D'Amico risk group classification to predict clinical progression as well as cancer-specific and overall survival after RRP using the largest series of surgical patients (7,591) evaluated using this model to date. As predictive models use retrospective analyses to prospectively predict the clinical outcome of patients who have not yet been treated, validation is an important mechanism to ensure that such models are able to predict the outcome for patients outside of the original dataset.

The study also demonstrates that RRP achieves durable local control and long-term CSS (median follow-up 7.7 years), even in patients classified as having high-risk disease. Primary surgical therapy for these patients moreover offers the ability to obtain accurate pathological information which may assist in the selection of patients for adjuvant therapies who might benefit the most from such treatments, and may avoid the application of these potentially harmful therapies to patients not likely to benefit from them.

Overall, this study demonstrates the continued value of the D'Amico risk group classification as a simple pretreatment prognostic model, based upon clinical features readily available to the treating physician, which may be valuable for patient counseling, to assist with the identification of potential candidates for multimodal treatments, and to ensure the comparability of treatment and control groups in clinical trials.

Written by Michael L. Blute, MD as part of Beyond the Abstract on UroToday.com. This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations, etc., of their research by referencing the published abstract.

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