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J Urol. 2006 Dec;176(6 Pt 2):S16-20.

Defining high risk prostate cancer with risk groups and nomograms: implications for designing clinical trials.

Author information

  • 1Department of Radiation Oncology, Comprehensive Cancer Center, University of California-San Francisco, 1600 Divisadero Street, San Francisco, CA 94143, USA. roach@radonc17.ucsf.edu

Abstract

PURPOSE:

Death from prostate cancer is usually preceded by metastases and it usually occurs in men with high risk disease who experienced biochemical failure with a short prostate specific antigen doubling time. We developed a model for determining disease specific survival in prostate cancer.

MATERIALS AND METHODS:

We used the model for defining high risk prostate cancer that was developed by the Radiation Therapy Oncology Group and combined it with the Kattan nomogram for predicting the risk of metastases. We selected 414 Radiation Therapy Oncology Group intermediate and high risk patients who were treated with external beam radiotherapy alone. Excluded were patients with low risk disease. The Kaplan-Meier product limit method was used to estimate the probability of freedom from biochemical failure, overall survival and disease specific survival.

RESULTS:

A significant difference was observed in freedom from biochemical failure, disease specific survival and overall survival among the 3 tertiles created by the nomogram using the cutoff points less than 8.5%, 8.5% to 15% and greater than 15% (p <0.001, 0.0002 and 0.0003, respectively). Only the risk of metastases using the categorized nomogram score (less than 8.5% and 8.5% to 15% vs greater than 15%), not preradiotherapy prostate specific antigen or Radiation Therapy Oncology Group risk (Radiation Therapy Oncology Group 2 vs 3), was a significant predictor of disease specific and overall survival for intermediate/high risk patients and intermediate/high risk with 15% or less risk for metastases.

CONCLUSIONS:

We combined a risk group stratification scheme for disease specific survival with a nomogram predicting the risk of metastases and created a model that may be useful for designing phase III trials with metastases and disease specific survival as study end points.

PMID:
17084158
[PubMed - indexed for MEDLINE]
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