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Predictive Value of the Cancer of the Prostate Risk Assessment Score for Recurrence-Free Survival After Radical Prostatectomy in Korea: A Single-Surgeon Series

´ëÇѺñ´¢±â°úÇÐȸÁö 2014³â 55±Ç 5È£ p.321 ~ 326
¼­¿øÀÍ, °­Çʹ®, Á¤ÀçÀÏ,
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¼­¿øÀÍ ( Seo Won-Ik ) 
Inje University Busan Paik Hospital Department of Urology

°­Çʹ® ( Kang Pil-Moon ) 
Inje University Busan Paik Hospital Department of Urology
Á¤ÀçÀÏ ( Chung Jae-Il ) 
Inje University College of Medicine Department of Urology

Abstract


Purpose: To evaluate the validity of the cancer of the prostate risk assessment (CAPRA) score, a newly developed nomogram for preoperative prediction of recurrence after radical prostatectomy, in a single institution in Korea.

Materials and Methods: We retrospectively studied 115 men who had undergone radical prostatectomy as the first treatment for localized prostate cancer. The validity of the CAPRA score for the prediction of recurrence-free survival (RFS) and pathologic outcome was evaluated by using Kaplan-Meier analysis and a proportional hazards regression model. A seven-group model and a three-group model were used for the results.

Results: None of the variables of the CAPRA score was favorable compared with the previously reported data. The three-group model was significantly related with 3- and 5-year RFS (p<0.05), but the seven-group model was not. The concordance indices of the CAPRA score were 0.74 and 0.77. Of four components excluding the clinical T stage, three independently predicted RFS (age, Gleason sum, and percentage of positive biopsies). The CAPRA score was significantly related to the margin status, extracapsular extension, and seminal vesicle invasion in both the seven- and three-group models. In the three-group model, pathologic outcomes were more strongly related, especially a higher risk of seminal vesicle invasion.

Conclusions: The CAPRA score showed high accuracy for predicting RFS. In particular, the three-group model was more useful for predicting RFS and pathologic outcomes. Therefore, the CAPRA score may be a useful prediction model for risk stratification and may help clinicians to develop localized prostate cancer treatment.

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Nomograms; Prostatectomy; Prostatic neoplasms; Recurrence

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