Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy

  • Andrew J. Stephenson
  • , Peter T. Scardino
  • , James A. Eastham
  • , Fernando J. Bianco
  • , Zohar A. Dotan
  • , Christopher J. DiBlasio
  • , Alwyn Reuther
  • , Eric A. Klein
  • , Michael W. Kattan*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

543 Scopus citations

Abstract

Purpose: A postoperative nomogram for prostate cancer recurrence after radical prostatectomy (RP) has been independently validated as accurate and discriminating. We have updated the nomogram by extending the predictions to 10 years after RP and have enabled the nomogram predictions to be adjusted for the disease-free interval that a patient has maintained after RP. Methods: Cox regression analysis was used to model the clinical information for 1,881 patients who underwent RP for clinically-localized prostate cancer by two high-volume surgeons. The model was externally validated separately on two independent cohorts of 1,782 patients and 1,357 patients, respectively. Disease progression was defined as a rising prostate-specific antigen (PSA) level, clinical progression, radiotherapy more than 12 months postoperatively, or initiation of systemic therapy. Results: The 10-year progression-free probability for the modeling set was 79% (95% CI, 75% to 82%). Significant variables in the multivariable model included PSA (P = .002), primary (P < .0001) and secondary Gleason grade (P = .0006), extracapsular extension (P < .0001), positive surgical margins (P = .028), seminal vesicle invasion (P < .0001), lymph node involvement (P = .030), treatment year (P = .008), and adjuvant radiotherapy (P = .046). The concordance index of the nomogram when applied to the independent validation sets was 0.81 and 0.79. Conclusion: We have developed and validated as a robust predictive model an enhanced postoperative nomogram for prostate cancer recurrence after RP. Unique to predictive models, the nomogram predictions can be adjusted for the disease-free interval that a patient has achieved after RP.

Original languageEnglish
Pages (from-to)7005-7012
Number of pages8
JournalJournal of Clinical Oncology
Volume23
Issue number28
DOIs
StatePublished - 2005
Externally publishedYes

Funding

FundersFunder number
National Cancer InstituteP50CA092629

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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