Literature DB >> 11547066

Predicting risk of prostate specific antigen recurrence after radical prostatectomy with the Center for Prostate Disease Research and Cancer of the Prostate Strategic Urologic Research Endeavor databases.

J W Moul1, R R Connelly, D P Lubeck, J J Bauer, L Sun, S C Flanders, G D Grossfeld, P R Carroll.   

Abstract

PURPOSE: Biostatistical models to predict stage or outcome in patients with clinically localized prostate cancer with pretreatment prostate specific antigen (PSA), Gleason sum on biopsy or prostatectomy specimen, clinical or pathological stage and other variables, including ethnicity, have been developed. However, to date models have relied on small subsets from academic centers or military populations that may not be representative. Our study validates and updates a model published previously with the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE, UCSF, Urology Outcomes Research Group and TAP Pharmaceutical Products, Inc.), a large multicenter, community based prostate cancer database and Center for Prostate Disease Research (CPDR), a large military database.
MATERIALS AND METHODS: We validated a biostatistical model that includes pretreatment PSA, highest Gleason sum on prostatectomy specimen, prostatectomy organ confinement status and ethnicity, including white and black patients. We then revised it with the Cox regression analysis of the combined 503 PSA era surgical cases from the CPDR prospective cancer database and 1,012 from the CaPSURE prostate cancer outcomes database.
RESULTS: The original equation with 3 risk groups stratified CaPSURE cases into distinct categories with 7-year disease-free survival rates of 72%, 42.1% and 27.6% for low, intermediate and high risk men, respectively. Parameter estimates obtained from a Cox regression analysis provided a revised model equation that calculated the relative risk of recurrence as: exponent (exp)[(0.54 x Race) + (0.05 x sigmoidal transformation of PSA [PSA(ST)]) + (0.23 x Postop Gleason) + (0.69 x Pathologic stage). The relative risk of recurrence, as calculated by the aforementioned equation, was used to stratify the cases into 4 risk groups. Very low-4.7 or less, low-4.7 to 7.1, high-7.1 to 16.7 and very high-greater than 16.7, and patients at risk had 7-year disease-free survival rates of 85.4%, 66.0%, 50.6% and 21.3%, respectively.
CONCLUSIONS: With a broad cohort of community based, academic and military cases, we developed an equation that stratifies men into 4 discrete risk groups of recurrence after radical prostatectomy and confirmed use of a prior 3 risk group model. Although the variables of ethnicity, pretreatment PSA, highest Gleason sum on prostatectomy specimen and organ confinement status on surgical pathology upon which the model is based are easily obtained, more refined modeling with additional variables are needed to improve prediction of intermediate risk in individuals.

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Year:  2001        PMID: 11547066

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  18 in total

Review 1.  Global registries for measuring pharmacoeconomic and quality-of-life outcomes: focus on design and data collection, analysis and interpretation.

Authors:  Lisa Kennedy; Ann-Marie Craig
Journal:  Pharmacoeconomics       Date:  2004       Impact factor: 4.981

2.  Do nomograms designed to predict biochemical recurrence (BCR) do a better job of predicting more clinically relevant prostate cancer outcomes than BCR? A report from the SEARCH database group.

Authors:  Anna E Teeter; Joseph C Presti; William J Aronson; Martha K Terris; Christopher J Kane; Christopher L Amling; Stephen J Freedland
Journal:  Urology       Date:  2013-07       Impact factor: 2.649

3.  Dynamic prediction of metastases after radical prostatectomy for prostate cancer.

Authors:  Brant A Inman; Igor Frank; Stephen A Boorjian; Joseph W Akornor; R Jeffrey Karnes; Bradley C Leibovich; Michael L Blute; Eric J Bergstralh
Journal:  BJU Int       Date:  2011-05-26       Impact factor: 5.588

4.  Salvage radiotherapy for patients with PSA relapse following radical prostatectomy: issues and challenges.

Authors:  Richard Choo
Journal:  Cancer Res Treat       Date:  2010-03-31       Impact factor: 4.679

5.  The evolving definition of advanced prostate cancer.

Authors:  Judd W Moul
Journal:  Rev Urol       Date:  2004

6.  Postoperative prostate-specific antigen nadir improves accuracy for predicting biochemical recurrence after radical prostatectomy: Results from the Shared Equal Access Regional Cancer Hospital (SEARCH) and Duke Prostate Center databases.

Authors:  Daniel M Moreira; Joseph C Presti; William J Aronson; Martha K Terris; Christopher J Kane; Christopher L Amling; Leon L Sun; Judd W Moul; Stephen J Freedland
Journal:  Int J Urol       Date:  2010-09-30       Impact factor: 3.369

7.  Risk stratification in the hormonal treatment of patients with prostate cancer.

Authors:  Matthew A Uhlman; Judd W Moul; Ping Tang; Danielle A Stackhouse; Leon Sun
Journal:  Ther Adv Med Oncol       Date:  2009-09       Impact factor: 8.168

Review 8.  Critical review of prostate cancer predictive tools.

Authors:  Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino
Journal:  Future Oncol       Date:  2009-12       Impact factor: 3.404

9.  The relationship between preoperative prostate-specific antigen and biopsy Gleason sum in men undergoing radical retropubic prostatectomy: a novel assessment of traditional predictors of outcome.

Authors:  Phillip Pierorazio; Manisha Desai; Tara McCann; Mitchell Benson; James McKiernan
Journal:  BJU Int       Date:  2008-09-03       Impact factor: 5.588

10.  The effect of race on the discriminatory accuracy of models to predict biochemical recurrence after radical prostatectomy: results from the Shared Equal Access Regional Cancer Hospital and Duke Prostate Center databases.

Authors:  D M Moreira; J C Presti; W J Aronson; M K Terris; C J Kane; C L Amling; L L Sun; J W Moul; S J Freedland
Journal:  Prostate Cancer Prostatic Dis       Date:  2009-11-17       Impact factor: 5.554

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