title: Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network.
abstract: OBJECTIVE
  The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models.
  
  
  METHODS
  Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05).
  
  
  RESULTS
  The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer.
  
  
  CONCLUSIONS
  The performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, Seoul 110-744, Korea.
authors.forename: Sang Youn
authors.initials: SY
authors.lastname: Kim
authors.forename: Sung Kyoung
authors.initials: SK
authors.lastname: Moon
authors.forename: Dae Chul
authors.initials: DC
authors.lastname: Jung
authors.forename: Sung Il
authors.initials: SI
authors.lastname: Hwang
authors.forename: Chang Kyu
authors.initials: CK
authors.lastname: Sung
authors.forename: Jeong Yeon
authors.initials: JY
authors.lastname: Cho
authors.forename: Seung Hyup
authors.initials: SH
authors.lastname: Kim
authors.forename: Jiwon
authors.initials: J
authors.lastname: Lee
authors.forename: Hak Jong
authors.initials: HJ
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001707:Biopsy, Needle; D020000:Decision Support Systems, Clinical; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery; D012372:ROC Curve; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D017430:Prostate-Specific Antigen