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Predictive tools for prostate cancer staging, treatment response and outcomes.
MedLine Citation:
PMID:  23154603     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
OBJECTIVES: Numerous predictive models relating to prostate cancer staging and outcomes have been described. We sought to review and categorize these predictive tools to create a comprehensive reference for physicians who treat prostate cancer. METHODS: We performed a search of MEDLINE literature from January 1966 to April 2012 to identify predictive models relating to prostate cancer staging, treatment, and outcomes in the pre-treated patient. For each model identified, we describe the outcome predicted, the variables comprising the model, the size of the cohort on which the tool was developed, predictive discrimination estimates, and whether internal and/or external validation has been performed. RESULTS: We identified 80 predictive tools applicable to pre-treated prostate cancer patients, 30 of which had been externally validated. Tools designed to predict pathologic stage were the most common; several models focused on accurately predicting clinically insignificant prostate cancer while another large proportion focused on the prediction of locally advanced disease (i.e. extracapsular extension, seminal vesicle involvement, lymph node invasion). Other models described studied biochemical outcomes following radical prostatectomy, external beam radiotherapy, or brachytherapy. Very few models addressed the prediction of metastasis and survival. Finally, several tools incorporated novel pre-treatment serum biomarkers or magnetic resonance imaging findings into base models to enhance the accuracy of standard clinicopathologic variables. CONCLUSION: To deliver optimal, individualized prostate cancer care, treatment should be tailored to the specific characteristics of each patient and each tumor. Predictive models may facilitate such an approach and are numerously described in the literature. While the performance of predictive models is encouraging, further improvement through inclusion of biomarkers as well as evaluation of their clinical utility is imperative. Optimally, predictive models should be further studied in the prospective setting.
Authors:
David A Green; E Charles Osterberg; Evanguelos Xylinas; Michael Rink; Pierre I Karakiewicz; Douglas S Scherr; Shahrokh F Shariat
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Archivos espanoles de urologia     Volume:  65     ISSN:  1576-8260     ISO Abbreviation:  Arch. Esp. Urol.     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0064757     Medline TA:  Arch Esp Urol     Country:  -    
Other Details:
Languages:  ENG; SPA     Pagination:  787-807     Citation Subset:  -    
Affiliation:
Department of Urology. Weill Cornell Medical College. New York. USA.
Vernacular Title:
Herramientas predictoras del estadiaje del cáncer de próstata, respuesta al tratamiento y resultados.
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