title: Predicting prostate biopsy outcome: artificial neural networks and polychotomous regression are equivalent models.
abstract: BACKGROUND
  Complex statistical models utilizing multiple inputs to derive a risk assessment may benefit prostate cancer (PC) detection where focus has been on prostate-specific antigen (PSA). This study develops a polychotomous logistic regression (PR) model and an artificial neural network (ANN) for predicting biopsy results, particularly for clinically significant PC.
  
  
  METHODS
  There were 3,025 men undergoing TRUS-guided biopsy (BX) with PSA <10 ng/ml selected. BX outcome classified as benign, atypical small acinar proliferation or high-grade prostatic intraepithelial neoplasia (ASAP/PIN), non-significant (NSPC) or clinically significant PC (CSPC). PR and ANN models were developed to distinguish between BX categories. Predictors were age, PSA, abnormal digital rectal examination (DRE), positive transrectal ultrasound (TRUS) and prostate volume.
  
  
  RESULTS
  Among the BXs, 44% were benign, 14% ASAP/PIN, 16% NSPC and 25% CSPC. Median age, PSA and volume were 64 years, 5.7 ng/ml and 50 cc. TRUS lesion was present in 47%, and DRE was abnormal in 39%. PR and ANN models did not differ on percentage BX outcomes correctly predicted (55, 57%, respectively) and were equally poor for both ASAP/PIN (0%) and NSPC (2%). For PR and ANN, 74-78% ASAP/PIN predicted benign, 2% NSPC and 20-24% CSPC. For NSPC, 69-71% predicted benign, 27-29% CSPC. Benign outcomes were well identified (86-88%), although 12-13% classified CSPC. CSPC was correctly identified in 65-66% with misclassifications largely benign (33% for PR and ANN).
  
  
  CONCLUSIONS
  Neither PR nor ANN was able to distinguish between the four biopsy outcomes: ASAP/PIN and NSPC were not distinguished from benign or CSPC. ANN did not perform better than PR. Inclusion of additional predictors may increase the performance of statistical models in predicting BX outcome.
authors.affiliation: Department of Urology, University of Toronto, University Health Network, Toronto, ON, Canada. Lawrentschuk@gmail.com
authors.forename: Nathan
authors.initials: N
authors.lastname: Lawrentschuk
authors.forename: Gina
authors.initials: G
authors.lastname: Lockwood
authors.forename: Peter
authors.initials: P
authors.lastname: Davies
authors.forename: Andy
authors.initials: A
authors.lastname: Evans
authors.forename: Joan
authors.initials: J
authors.lastname: Sweet
authors.forename: Ants
authors.initials: A
authors.lastname: Toi
authors.forename: Neil E
authors.initials: NE
authors.lastname: Fleshner
mesh_terms: D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000379:methods*; D019160:Endosonography; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D009864:Ontario / Q000453:epidemiology; D011237:Predictive Value of Tests; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*
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