OBJECTIVES: To use two population-based samples of prostate cancer-free men to develop and validate a novel multivariable equation for estimating prostate volume (PV). Previous investigators have demonstrated the ability to use serum prostate-specific antigen (PSA) levels to estimate PV in men without prostate cancer; however, the ability of additional clinical variables to further enhance PV estimation in these men remains unclear. METHODS: We applied linear regression modeling to data from an 80% random sample (n = 366) of the baseline cohort from the Olmsted County Study of Urinary Symptoms and Health Status among Men (OCS) to develop an equation for estimating PV in men without prostate cancer. We then evaluated the predictive ability of this equation by comparing estimated and measured PV values in 3 additional validation sets of men. RESULTS: The final linear regression model included PSA, age, and weight as independent predictors of PV. For prediction in baseline OCS men, the multiple correlation coefficients increased from 0.62(PSAalone) to 0.71(fullmodel). In addition, the area under the curve estimates from the receiver operating characteristic curves increased from 0.79(PSAalone) to 0.85(fullmodel) for predicting PV >30 mL. CONCLUSIONS: Our data suggest that PV can be estimated with easily obtained clinical variables. Moreover, we demonstrate that age and weight can be added to PSA level to achieve greater accuracy in predicting PV. This methodology may prove useful for estimating PV in men in settings where costs and practicality preclude the use of imaging techniques.
OBJECTIVES: To use two population-based samples of prostate cancer-free men to develop and validate a novel multivariable equation for estimating prostate volume (PV). Previous investigators have demonstrated the ability to use serum prostate-specific antigen (PSA) levels to estimate PV in men without prostate cancer; however, the ability of additional clinical variables to further enhance PV estimation in these men remains unclear. METHODS: We applied linear regression modeling to data from an 80% random sample (n = 366) of the baseline cohort from the Olmsted County Study of Urinary Symptoms and Health Status among Men (OCS) to develop an equation for estimating PV in men without prostate cancer. We then evaluated the predictive ability of this equation by comparing estimated and measured PV values in 3 additional validation sets of men. RESULTS: The final linear regression model included PSA, age, and weight as independent predictors of PV. For prediction in baseline OCS men, the multiple correlation coefficients increased from 0.62(PSAalone) to 0.71(fullmodel). In addition, the area under the curve estimates from the receiver operating characteristic curves increased from 0.79(PSAalone) to 0.85(fullmodel) for predicting PV >30 mL. CONCLUSIONS: Our data suggest that PV can be estimated with easily obtained clinical variables. Moreover, we demonstrate that age and weight can be added to PSA level to achieve greater accuracy in predicting PV. This methodology may prove useful for estimating PV in men in settings where costs and practicality preclude the use of imaging techniques.
Authors: Michael A Joseph; John T Wei; Sioban D Harlow; Kathleen A Cooney; Rodney L Dunn; Craig A Jaffe; James E Montie; David Schottenfeld Journal: Prostate Date: 2002-12-01 Impact factor: 4.104
Authors: J T Wei; D Schottenfeld; K Cooper; J M Taylor; G J Faerber; M A Velarde; R Bree; J E Montie; K A Cooney Journal: J Urol Date: 2001-05 Impact factor: 7.450
Authors: C A Mochtar; L A L M Kiemeney; M M van Riemsdijk; G S Barnett; M P Laguna; F M J Debruyne; J J M C H de la Rosette Journal: Eur Urol Date: 2003-12 Impact factor: 20.096
Authors: Nitin K Yerram; Dmitry Volkin; Baris Turkbey; Jeffrey Nix; Anthony N Hoang; Srinivas Vourganti; Gopal N Gupta; W Marston Linehan; Peter L Choyke; Bradford J Wood; Peter A Pinto Journal: BJU Int Date: 2012-11-06 Impact factor: 5.588