title: Prostate health index (PHI) and prostate-specific antigen (PSA) predictive models for prostate cancer in the Chinese population and the role of digital rectal examination-estimated prostate volume.
abstract: OBJECTIVE
  To investigate PSA- and PHI (prostate health index)-based models for prediction of prostate cancer (PCa) and the feasibility of using DRE-estimated prostate volume (DRE-PV) in the models.
  
  
  METHODS
  This study included 569 Chinese men with PSA 4-10 ng/mL and non-suspicious DRE with transrectal ultrasound (TRUS) 10-core prostate biopsies performed between April 2008 and July 2015. DRE-PV was estimated using 3 pre-defined classes: 25, 40, or 60 ml. The performance of PSA-based and PHI-based predictive models including age, DRE-PV, and TRUS prostate volume (TRUS-PV) was analyzed using logistic regression and area under the receiver operating curves (AUC), in both the whole cohort and the screening age group of 55-75.
  
  
  RESULTS
  PCa and high-grade PCa (HGPCa) was diagnosed in 10.9 % (62/569) and 2.8 % (16/569) men, respectively. The performance of DRE-PV-based models was similar to TRUS-PV-based models. In the age group 55-75, the AUCs for PCa of PSA alone, PSA with DRE-PV and age, PHI alone, PHI with DRE-PV and age, and PHI with TRUS-PV and age were 0.54, 0.71, 0.76, 0.78, and 0.78, respectively. The corresponding AUCs for HGPCa were higher (0.60, 0.70, 0.85, 0.83, and 0.83). At 10 and 20 % risk threshold for PCa, 38.4 and 55.4 % biopsies could be avoided in the PHI-based model, respectively.
  
  
  CONCLUSIONS
  PHI had better performance over PSA-based models and could reduce unnecessary biopsies. A DRE-assessed PV can replace TRUS-assessed PV in multivariate prediction models to facilitate clinical use.
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: Peter K F
authors.initials: PK
authors.lastname: Chiu
authors.affiliation: Department of Urology, Erasmus University Medical Centre, Rotterdam, The Netherlands.
authors.forename: Monique J
authors.initials: MJ
authors.lastname: Roobol
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: Jeremy Y
authors.initials: JY
authors.lastname: Teoh
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: Wai-Man
authors.initials: WM
authors.lastname: Lee
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: Siu-Ying
authors.initials: SY
authors.lastname: Yip
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: See-Ming
authors.initials: SM
authors.lastname: Hou
authors.affiliation: Department of Urology, Erasmus University Medical Centre, Rotterdam, The Netherlands.
authors.forename: Chris H
authors.initials: CH
authors.lastname: Bangma
authors.affiliation: Division of Urology, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China. ngcf@surgery.cuhk.edu.hk.|Department of Surgery, SH Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong SAR, China. ngcf@surgery.cuhk.edu.hk.
authors.forename: Chi-Fai
authors.initials: CF
authors.lastname: Ng
mesh_terms: D000368:Aged; D002681:China; D051517:Digital Rectal Examination / Q000379:methods*; D005240:Feasibility Studies; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D011467:Prostate / Q000473:pathology*; D017430:Prostate-Specific Antigen / Q000032:analysis*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods
chemical_list: D017430:Prostate-Specific Antigen
keywords: Prostate cancer; Prostate health index; Prostate volume; Prostate-specific antigen; Rectal examination; Risk calculator