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Prostate Cancer

Nomogram Predicting Prostate Cancer–specific Mortality for Men with Biochemical Recurrence After Radical Prostatectomy

By: John A. Brockman a b , Shaheen Alanee c , Andrew J. Vickers c d , Peter T. Scardino c , David P. Wood e , Adam S. Kibel f , Daniel W. Lin g , Fernando J. Bianco Jr. h , Danny M. Rabah i , Eric A. Klein a j , Jay P. Ciezki j , Tianming Gao k , Michael W. Kattan k and Andrew J. Stephenson a j lowast

European Urology, Volume 67 Issue 6, June 2015, Pages 1160-1167

Published online: 01 June 2015

Keywords: Prostatic neoplasms, Prostatectomy, Statistical models

Abstract Full Text Full Text PDF (2,4 MB) Patient Summary

Abstract

Background

The natural history of prostate-specific antigen (PSA)-defined biochemical recurrence (BCR) of prostate cancer (PCa) after definitive local therapy is highly variable. Validated prediction models for PCa-specific mortality (PCSM) in this population are needed for treatment decision-making and clinical trial design.

Objective

To develop and validate a nomogram to predict the probability of PCSM from the time of BCR among men with rising PSA levels after radical prostatectomy.

Design, setting, and participants

Between 1987 and 2011, 2254 men treated by radical prostatectomy at one of five high-volume hospitals experienced BCR, defined as three successive PSA rises (final value >0.2 ng/ml), single PSA >0.4 ng/ml, or use of secondary therapy administered for detectable PSA >0.1 ng/ml. Clinical information and follow-up data were modeled using competing-risk regression analysis to predict PCSM from the time of BCR.

Intervention

Radical prostatectomy for localized prostate cancer and subsequent PCa BCR.

Outcome measurements and statistical analysis

PCSM.

Results and limitations

The 10-yr PCSM and mortality from competing causes was 19% (95% confidence interval [CI] 16–21%) and 17% (95% CI 14–19%), respectively. A nomogram predicting PCSM for all patients had an internally validated concordance index of 0.774. Inclusion of PSA doubling time (PSADT) in a nomogram based on standard parameters modestly improved predictive accuracy (concordance index 0.763 vs 0.754). Significant parameters in the models were preoperative PSA, pathological Gleason score, extraprostatic extension, seminal vesicle invasion, time to PCa BCR, PSA level at PCa BCR, and PSADT (allp < 0.05).

Conclusions

We constructed and validated a nomogram to predict the risk of PCSM at 10 yr among men with PCa BCR after radical prostatectomy. The nomogram may be used for patient counseling and the design of clinical trials for PCa.

Patient summary

For men with biochemical recurrence of prostate cancer after radical prostatectomy, we have developed a model to predict the long-term risk of death from prostate cancer.

Take Home Message

Using a multi-institutional cohort of 2254 men with rising prostate-specific antigen (PSA) after radical prostatectomy, we developed and validated a robust nomogram to predict the long-term risk of prostate cancer–specific mortality from the time of biochemical recurrence. PSA doubling time contributed little to the accuracy of the model.

Keywords: Prostatic neoplasms, Prostatectomy, Statistical models.

Footnotes

a Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA

b Case Western Reserve University School of Medicine, Cleveland, OH, USA

c Division of Urology, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

d Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

e Department of Urology, University of Michigan, Ann Arbor, MI, USA

f Division of Urologic Surgery, Washington University School of Medicine, St. Louis, MO, USA

g Department of Urology, University of Washington School of Medicine, Seattle, WA, USA

h Department of Urology, Columbia University, New York, NY, USA

i Division of Urology, Department of Surgery, Princess Johara Alibrahim Center for Cancer Research, King Saud University, Riyadh, Saudi Arabia

j Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA

k Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA

lowast Corresponding author. Cleveland Clinic, 9500 Euclid Avenue, Desk Q10-1, Cleveland, OH 44195-0001, USA. Tel. +1 216 4451062; Fax: +1 216 6364492.

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