title: [Establishment of a scoring system for predicting the positive rate of prostatic biopsy for prostate cancer].
abstract: OBJECTIVE
  To identify the predictors of the positive results of transrectal ultrasound (TRUS)-guided biopsy for prostate cancer.
  
  
  METHODS
  We performed univariate and multivariate logistic regression analyses on the relevant data on 385 male patients that underwent TRUS-guided biopsy for prostate cancer, including such potential predictors as age, body mass index (BMI), symptoms, results of digital rectal examination (DRE), tPSA, fPSA, free/total PSA ratio (f/tPSA), prostate volume (PV), and PSA density (PSAD) for identification of the risk factors related to the positive rate of biopsy. Then we constructed a scoring system as a tool for predicting prostate cancer in repeat biopsies and determined the sensitivity of the system by calculating the false positive rate using the receiver operating characteristic curve.
  
  
  RESULTS
  Among the 385 patients, 139 (36.1%) were diagnosed with prostate cancer. On multivariate analysis, age (P < 0.01), DRE (P < 0.01), tPSA (P < 0.01), fPSA (P < 0.01), f/tPSA (P < 0.01), PV (P < 0.01), and PSAD (P < 0.01) were all significant predictors of prostate cancer. Multivariate logistic regression analysis showed age, tPSA, f/tPSA, PV, and PSAD to be independent predictors, with ORs and 95% CIs of 1.07 (1.05-1.16), 1.05 (1.02-1.15), 0.97 (0.86-0.99), 0.98 (0.87-0.96), and 1.79 (1.48-2.06), respectively. Moreover, patients with the risk score of 3-5 had a significantly higher rate of prostate cancer than those with 0-2 (64% vs 11%, P < 0.001).
  
  
  CONCLUSIONS
  The scoring system on the key predictors of prostate cancer can help urologists to identify the men in need of prostatic biopsy.
authors.forename: Jian-gang
authors.initials: JG
authors.lastname: Chen
authors.forename: Xin-feng
authors.initials: XF
authors.lastname: Chen
authors.forename: Dong-hua
authors.initials: DH
authors.lastname: Gu
authors.forename: Ming
authors.initials: M
authors.lastname: Lu
authors.forename: Bing
authors.initials: B
authors.lastname: Zheng
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
authors.forename: Bin
authors.initials: B
authors.lastname: Pan
authors.forename: Hua
authors.initials: H
authors.lastname: Zhu
authors.forename: Xiao-dong
authors.initials: XD
authors.lastname: Pan
authors.forename: Bin
authors.initials: B
authors.lastname: Xu
authors.forename: Lin
authors.initials: L
authors.lastname: Qian
mesh_terms: D000368:Aged; D051517:Digital Rectal Examination; D006801:Humans; D061705:Image-Guided Biopsy / Q000379:methods; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011467:Prostate / Q000473:pathology*; D017430:Prostate-Specific Antigen / Q000032:analysis; D011471:Prostatic Neoplasms / Q000737:chemistry / Q000473:pathology*; D012372:ROC Curve; D012307:Risk Factors; D018084:Ultrasonography, Interventional / Q000379:methods
chemical_list: D017430:Prostate-Specific Antigen