title: Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine.
abstract: OBJECTIVE
  We developed a multiple logistic regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model.
  
  
  METHODS
  One thousand and seventy-seven consecutive patients who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were enrolled in the study. Clinical decision models were constructed from the input data of age, digital rectal examination findings, prostate-specific antigen (PSA), PSA density (PSAD), PSAD in transitional zone, and TRUS findings. The patients were divided into the training and test groups in a randomized fashion. Areas under the receiver operating characteristic (ROC) curve (AUC, Az) were calculated to summarize the overall performance of each decision model for the task of prostate cancer prediction.
  
  
  RESULTS
  The Az values of the ROC curves for the use of multiple logistic regression analysis, ANN, and the SVM were 0.768, 0.778, and 0.847, respectively. Pairwise comparison of the ROC curves determined that the performance of the SVM was superior to that of the ANN or the multiple logistic regression model.
  
  
  CONCLUSIONS
  Image-based clinical decision support models allow patients to be informed of the actual probability of having a prostate cancer.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
authors.forename: Hak Jong
authors.initials: HJ
authors.lastname: Lee
authors.forename: Sung Il
authors.initials: SI
authors.lastname: Hwang
authors.forename: Seok-Min
authors.initials: SM
authors.lastname: Han
authors.forename: Seong Ho
authors.initials: SH
authors.lastname: Park
authors.forename: Seung Hyup
authors.initials: SH
authors.lastname: Kim
authors.forename: Jeong Yeon
authors.initials: JY
authors.lastname: Cho
authors.forename: Chang Gyu
authors.initials: CG
authors.lastname: Seong
authors.forename: Gheeyoung
authors.initials: G
authors.lastname: Choe
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012007:Rectum / Q000000981:diagnostic imaging; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*