title: Individualized estimates of overdiagnosis in screen-detected prostate cancer.
abstract: BACKGROUND
  The chance that a prostate cancer detected by screening is overdiagnosed (ie, it would not have been detected in the absence of screening) can vary widely depending on the patient's age and tumor characteristics. The purpose of this study is to use age, Gleason score, and prostate-specific antigen (PSA) level to help inform patients with screen-detected prostate cancers about the chances their cancers were overdiagnosed.
  
  
  METHODS
  A computer microsimulation model of prostate cancer natural history was used to generate virtual life histories in the presence and absence of PSA screening, including an indicator of whether screen-detected cancers are overdiagnosed. A logistic regression model was fit to nonmetastatic patients diagnosed by screening with PSA less than 10 ng/mL, and a nomogram was created to predict the individualized risk of overdiagnosis given age, Gleason score, and PSA at diagnosis.
  
  
  RESULTS
  The calibrated microsimulation model closely reproduces observed incidence trends in the Surveillance, Epidemiology, and End Results registries by age, stage, and Gleason score. The fitted logistic regression predicts risks of overdiagnosis among PSA-detected patients with an area under the curve of 0.75. Chances of overdiagnosis range from 2.9% to 88.1%.
  
  
  CONCLUSIONS
  The chances of overdiagnosis vary considerably by age, Gleason score, and PSA at diagnosis. The overdiagnosis nomogram presents tailored estimates of these risks based on patient and tumor information known at diagnosis and can be used to inform decisions about treating PSA-detected prostate cancers.
authors.affiliation: Affiliations of authors: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (RG, JK, RE); Department of Biostatistics (LYTI) and Department of Urology (JLG), University of Washington, Seattle, WA.
authors.forename: Roman
authors.initials: R
authors.lastname: Gulati
authors.forename: Lurdes Y T
authors.initials: LY
authors.lastname: Inoue
authors.forename: John L
authors.initials: JL
authors.lastname: Gore
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Katcher
authors.forename: Ruth
authors.initials: R
authors.lastname: Etzioni
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood*; D003198:Computer Simulation; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D049451:Nomograms; D011237:Predictive Value of Tests; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000276:immunology / Q000473:pathology / Q000517:prevention & control; D012306:Risk; D014481:United States; D057832:Watchful Waiting
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen