title: Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.
abstract: OBJECTIVE
  Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment.
  
  
  METHODS
  From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models.
  
  
  RESULTS
  The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively.
  
  
  CONCLUSIONS
  The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment.
authors.affiliation: Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Ja-Der
authors.initials: JD
authors.lastname: Liang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Xiao-Ou
authors.initials: XO
authors.lastname: Ping
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Children's Hospital Boston, Boston, MA, USA.
authors.forename: Yi-Ju
authors.initials: YJ
authors.lastname: Tseng
authors.affiliation: Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Guan-Tarn
authors.initials: GT
authors.lastname: Huang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Feipei
authors.initials: F
authors.lastname: Lai
authors.affiliation: Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. Electronic address: pmyang@ntuh.gov.tw.
authors.forename: Pei-Ming
authors.initials: PM
authors.lastname: Yang
mesh_terms: D000368:Aged; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000532:radiotherapy*; D017115:Catheter Ablation / Q000379:methods*; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*; D008297:Male; D008490:Medical Informatics / Q000379:methods; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D011846:Radio Waves; D012008:Recurrence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Feature selection; Hepatocellular carcinoma; Predictive model; Radiofrequency ablation; Support vector machine