BACKGROUND:Prostate-specific antigen (PSA) testing is the primary method used to diagnose prostate cancer in the United States. Methods to integrate other risk factors associated with prostate cancer into individualized risk prediction are needed. We used prostate biopsy data from men who participated in the Prostate Cancer Prevention Trial (PCPT) to develop a predictive model of prostate cancer. METHODS: We included 5519 men from the placebo group of the PCPT who underwent prostate biopsy, had at least one PSA measurement and a digital rectal examination (DRE) performed during the year before the biopsy, and had at least two PSA measurements performed during the 3 years before the prostate biopsy. Logistic regression was used to model the risk of prostate cancer and high-grade disease associated with age at biopsy, race, family history of prostate cancer, PSA level, PSA velocity, DRE result, and previous prostate biopsy. Risk equations were created from the estimated logistic regression models. All statistical tests were two-sided. RESULTS:A total of 1211 (21.9%) men were diagnosed with prostate cancer by prostate biopsy. Variables that predicted prostate cancer included higher PSA level, positive family history of prostate cancer, and abnormal DRE result, whereas a previous negative prostate biopsy was associated with reduced risk. Neither age at biopsy nor PSA velocity contributed independent prognostic information. Higher PSA level, abnormal DRE result, older age at biopsy, and African American race were predictive for high-grade disease (Gleason score > or =7) whereas a previous negative prostate biopsy reduced this risk. CONCLUSIONS: This predictive model allows an individualized assessment of prostate cancer risk and risk of high-grade disease for men who undergo a prostate biopsy.
RCT Entities:
BACKGROUND:Prostate-specific antigen (PSA) testing is the primary method used to diagnose prostate cancer in the United States. Methods to integrate other risk factors associated with prostate cancer into individualized risk prediction are needed. We used prostate biopsy data from men who participated in the Prostate Cancer Prevention Trial (PCPT) to develop a predictive model of prostate cancer. METHODS: We included 5519 men from the placebo group of the PCPT who underwent prostate biopsy, had at least one PSA measurement and a digital rectal examination (DRE) performed during the year before the biopsy, and had at least two PSA measurements performed during the 3 years before the prostate biopsy. Logistic regression was used to model the risk of prostate cancer and high-grade disease associated with age at biopsy, race, family history of prostate cancer, PSA level, PSA velocity, DRE result, and previous prostate biopsy. Risk equations were created from the estimated logistic regression models. All statistical tests were two-sided. RESULTS: A total of 1211 (21.9%) men were diagnosed with prostate cancer by prostate biopsy. Variables that predicted prostate cancer included higher PSA level, positive family history of prostate cancer, and abnormal DRE result, whereas a previous negative prostate biopsy was associated with reduced risk. Neither age at biopsy nor PSA velocity contributed independent prognostic information. Higher PSA level, abnormal DRE result, older age at biopsy, and African American race were predictive for high-grade disease (Gleason score > or =7) whereas a previous negative prostate biopsy reduced this risk. CONCLUSIONS: This predictive model allows an individualized assessment of prostate cancer risk and risk of high-grade disease for men who undergo a prostate biopsy.
Authors: Jingyu Zhang; Brian T Denton; Hari Balasubramanian; Nilay D Shah; Brant A Inman Journal: Med Decis Making Date: 2011-09-20 Impact factor: 2.583
Authors: Manthos G Giannoulis; Finbarr C Martin; K Sreekumaran Nair; A Margot Umpleby; Peter Sonksen Journal: Endocr Rev Date: 2012-03-20 Impact factor: 19.871
Authors: Florian R Schroeck; Kimberly A Zuhlke; Javed Siddiqui; Rabia Siddiqui; Kathleen A Cooney; John T Wei Journal: J Urol Date: 2012-10-02 Impact factor: 7.450
Authors: D J Lundon; B D Kelly; R Foley; S Loeb; J M Fitzpatrick; R W G Watson; E Rogers; G C Durkan; K Walsh Journal: World J Urol Date: 2014-08-05 Impact factor: 4.226