title: Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy.
abstract: OBJECTIVE
  Using cohorts examined by extended biopsy, we developed and validated multivariate models predicting prostate cancer on initial biopsy and examined whether these extended biopsy-based models outperform previously established models.
  
  
  METHODS
  Initial extended biopsy (median 22 cores) was performed in 1509 Japanese men including 1083 at Tokyo Medical and Dental University Hospital (TMDU) and 426 at Cancer Institute Hospital (CIH). Logistic regression-based nomograms 1 and artificial neural network (ANN) 1 incorporating age, digital rectal examination, and prostate-specific antigen (PSA) and free PSA, and nomogram 2 and ANN2 further incorporating transrectal ultrasound (TRUS) findings and prostate volume were constructed on the TMDU data. These and previously established models were externally validated on the CIH data set and predictive accuracy was compared directly.
  
  
  RESULTS
  Without TRUS-derived information, nomogram 1 outperformed the ANN1. With TRUS-derived information, nomogram 2 was more accurate than ANN2. External validation revealed applicability of the Western models to Japanese population, superiority of the nomograms over ANN models, and better predictive accuracy of our extended biopsy-based nomograms than the previous 6-10-core biopsy-based models. Using nomograms 1 and 2, 16% and 19% unnecessary biopsies would be saved at 95% sensitivity.
  
  
  CONCLUSIONS
  We developed new nomograms predicting prostate cancer on initial biopsy in men with PSA <20ng/ml. Predictive accuracy of these extended biopsy-based nomograms is better than those of previously established models based on 6-10-core biopsies. Our models might help clinicians to decide if a patient requires biopsy and to avoid unnecessary biopsies.
authors.affiliation: Department of Urology, Graduate School, Tokyo Medical and Dental University, Tokyo, Japan. s-kawakami@tmd.ac.jp
authors.forename: Satoru
authors.initials: S
authors.lastname: Kawakami
authors.forename: Noboru
authors.initials: N
authors.lastname: Numao
authors.forename: Yuhei
authors.initials: Y
authors.lastname: Okubo
authors.forename: Fumitaka
authors.initials: F
authors.lastname: Koga
authors.forename: Shinya
authors.initials: S
authors.lastname: Yamamoto
authors.forename: Kazutaka
authors.initials: K
authors.lastname: Saito
authors.forename: Yasuhisa
authors.initials: Y
authors.lastname: Fujii
authors.forename: Junji
authors.initials: J
authors.lastname: Yonese
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Masuda
authors.forename: Kazunori
authors.initials: K
authors.lastname: Kihara
authors.forename: Iwao
authors.initials: I
authors.lastname: Fukui
mesh_terms: D000367:Age Factors; D000368:Aged; D001706:Biopsy / Q000379:methods; D051517:Digital Rectal Examination; D006801:Humans; D007564:Japan; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D049451:Nomograms; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000473:pathology*; D012372:ROC Curve; D018084:Ultrasonography, Interventional
chemical_list: D017430:Prostate-Specific Antigen