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Eur Urol. 2006 Apr;49(4):666-74. Epub 2006 Jan 6.

Pre-treatment nomogram for disease-specific survival of patients with chemotherapy-naive androgen independent prostate cancer.

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1
Department of Urology, The University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Blvd. J8.112, Dallas, Texas 75390-9110, USA.

Abstract

OBJECTIVE:

Our objective was to develop a nomogram that predicts the probability of cancer-specific survival in men with untreated androgen-independent prostate cancer (AIPC).

METHODS:

AIPC was diagnosed in 129 consecutive patients between 1989 and 2002. No patient received cytotoxic chemotherapy. Univariate and multivariate Cox regression models were used to test the association between prostate-specific antigen (PSA) level at initiation of androgen deprivation, PSA doubling time (PSADT), PSA nadir on androgen deprivation therapy (ADT), time from ADT to AIPC, and AIPC-specific mortality. Multivariate regression coefficients were then used to develop a nomogram predicting AIPC-specific survival at 12-60 mo after AIPC diagnosis. Two-hundred bootstrap resamples were used to internally validate the nomogram.

RESULTS:

AIPC-specific mortality was recorded in 74 of 129 patients (57.4%). Other-cause mortality was recorded in 7 men (5.4%). Median overall survival was 52.0 mo (mean, 36.0 mo) and median AIPC-specific survival was 54.0 mo (mean, 35.0 mo). In univariate regression models, all variables were significant predictors of AIPC-specific survival (p < or = 0.02). In multivariate models, PSADT and time from androgen deprivation to AIPC remained statistically significant (p < or = 0.004). Bootstrap-corrected predictive accuracy of the nomogram was 80.9% versus 74.9% for our previous model.

CONCLUSIONS:

A nomogram predicting AIPC-specific survival is between 13% and 14% more accurate than previous nomograms and 6% more accurate than tree regression-based predictions obtained from the same data. Moreover, a nomogram approach combines several advantages, such as user-friendly interface and precise estimation of individual recurrence probability at several time points after AIPC diagnosis, which all patients deserve to know and all treating physicians need to know.

PMID:
16423446
DOI:
10.1016/j.eururo.2005.11.032
[Indexed for MEDLINE]
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