title: Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study.
abstract: OBJECTIVE
  Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations.
  
  
  METHODS
  Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients.
  
  
  RESULTS
  The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%.
  
  
  CONCLUSIONS
  It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.
authors.affiliation: Josephine Ford Cancer Center and Department of Urology, Henry Ford Medical Center, Detroit, Michigan 48202, USA. Atiwari1@hfhs.org
authors.forename: A
authors.initials: A
authors.lastname: Tewari
authors.forename: M
authors.initials: M
authors.lastname: Issa
authors.forename: R
authors.initials: R
authors.lastname: El-Galley
authors.forename: H
authors.initials: H
authors.lastname: Stricker
authors.forename: J
authors.initials: J
authors.lastname: Peabody
authors.forename: J
authors.initials: J
authors.lastname: Pow-Sang
authors.forename: A
authors.initials: A
authors.lastname: Shukla
authors.forename: Z
authors.initials: Z
authors.lastname: Wajsman
authors.forename: M
authors.initials: M
authors.lastname: Rubin
authors.forename: J
authors.initials: J
authors.lastname: Wei
authors.forename: J
authors.initials: J
authors.lastname: Montie
authors.forename: R
authors.initials: R
authors.lastname: Demers
authors.forename: C C
authors.initials: CC
authors.lastname: Johnson
authors.forename: L
authors.initials: L
authors.lastname: Lamerato
authors.forename: G W
authors.initials: GW
authors.lastname: Divine
authors.forename: E D
authors.initials: ED
authors.lastname: Crawford
authors.forename: E J
authors.initials: EJ
authors.lastname: Gamito
authors.forename: R
authors.initials: R
authors.lastname: Farah
authors.forename: P
authors.initials: P
authors.lastname: Narayan
authors.forename: G
authors.initials: G
authors.lastname: Carlson
authors.forename: M
authors.initials: M
authors.lastname: Menon
mesh_terms: D000368:Aged; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008957:Models, Genetic; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood / Q000601:surgery*
chemical_list: D017430:Prostate-Specific Antigen