title: Two-Stage Biomarker Protocols for Improving the Precision of Early Detection of Prostate Cancer.
abstract: New cancer biomarkers are being discovered at a rapid pace; however, these tests vary in their predictive performance characteristics, and it is unclear how best to use them.
  
  
  
  We investigated 2-stage biomarker-based screening strategies in the context of prostate cancer using a partially observable Markov model to simulate patients' progression through prostate cancer states to mortality from prostate cancer or other causes. Patients were screened every 2 years from ages 55 to 69. If the patient's serum prostate-specific antigen (PSA) was over a specified threshold in the first stage, a second stage biomarker test was administered. We evaluated design characteristics for these 2-stage strategies using 7 newly discovered biomarkers as examples. Monte Carlo simulation was used to estimate the number of screening biopsies, prostate cancer deaths, and quality-adjusted life-years (QALYs) per 1000 men.
  
  
  
  The all-cancer biomarkers significantly underperformed the high-grade cancer biomarkers in terms of QALYs. The screening strategy that used a PSA threshold of 2 ng/mL and a second biomarker test with high-grade sensitivity and specificity of 0.86 and 0.62, respectively, maximized QALYs. This strategy resulted in a prostate cancer death rate within 1% of using PSA alone with a threshold of 2 ng/mL, while reducing the number of biopsies by 20%. Sensitivity analysis suggests that the results are robust with respect to variation in model parameters.
  
  
  
  Two-stage biomarker screening strategies using new biomarkers with risk thresholds optimized for high-grade cancer detection may increase quality-adjusted survival and reduce unnecessary biopsies.
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI (CLB, BTD).
authors.forename: Christine L
authors.initials: CL
authors.lastname: Barnett
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI (SAT).
authors.forename: Scott A
authors.initials: SA
authors.lastname: Tomlins
authors.affiliation: Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC (DJU).
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Underwood
authors.affiliation: Department of Urology, University of Michigan, Ann Arbor, MI (SAT, JTW, TMM, JEM, BTD).
authors.forename: John T
authors.initials: JT
authors.lastname: Wei
authors.affiliation: Department of Urology, University of Michigan, Ann Arbor, MI (SAT, JTW, TMM, JEM, BTD).
authors.forename: Todd M
authors.initials: TM
authors.lastname: Morgan
authors.affiliation: Department of Urology, University of Michigan, Ann Arbor, MI (SAT, JTW, TMM, JEM, BTD).
authors.forename: James E
authors.initials: JE
authors.lastname: Montie
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI (CLB, BTD).|Department of Urology, University of Michigan, Ann Arbor, MI (SAT, JTW, TMM, JEM, BTD).
authors.forename: Brian T
authors.initials: BT
authors.lastname: Denton
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D020000:Decision Support Systems, Clinical; D055088:Early Detection of Cancer / Q000379:methods* / Q000592:standards; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D019057:Quality-Adjusted Life Years; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen
keywords: Biomarkers; Markov Model; Prostate Cancer; Simulation