title: Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation.
abstract: OBJECTIVE
  Radiofrequency ablation (RFA) provides an effective treatment for patients who exhibit early hepatocellular carcinoma (HCC) stages or are waiting for liver transplantation. It is important to assess patients after RFA. The goal of this study was to build artificial neural network models with HCC-related variables to predict the 1-year and 2-year disease-free survival (DFS) of HCC patients receiving RFA treatments.
  
  
  METHODS
  This study was a retrospective study that tracked HCC patients who received computer tomography-guided percutaneous RFA between January 2009 and April 2012. The numbers of total patients with 1-year and 2-year DFS were 252 and 179, respectively. A total of 15 HCC clinical variables were collected for the construction of artificial neural network models for DFS prediction. Internal validation and validation conducted using simulated prospective data were performed.
  
  
  RESULTS
  The results showed that the model with 15 inputs showed better performance compared with the models including only significant features. Parameters for performance assessment of 1-year DFS prediction were as follows: accuracy 85.0% (70.0%), sensitivity 75.0% (63.3%), specificity 87.5% (71.8%), and area under the curve 0.84 (0.77) for internal validation (simulated prospective validation). For 2-year DFS prediction, the values of accuracy, sensitivity, specificity, and area under the curve were 67.9% (63.9%), 50.0% (56.3%), 85.7% (70.0%), and 0.75 (0.72), respectively, for internal validation (simulated prospective validation).
  
  
  CONCLUSIONS
  This study revealed that the proposed artificial neural network models constructed with 15 clinical HCC relevant features could achieve an acceptable prediction performance for DFS. Such models can support clinical physicians to deal with clinical decision-making processes on the prognosis of HCC patients receiving RFA treatments.
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Chiueng-Fang
authors.initials: CF
authors.lastname: Wu
authors.affiliation: Department of Medical Imaging, Renai Branch, Taipei City Hospital, Taipei, Taiwan.
authors.forename: Yu-Jen
authors.initials: YJ
authors.lastname: Wu
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Po-Chin
authors.initials: PC
authors.lastname: Liang
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Chih-Horng
authors.initials: CH
authors.lastname: Wu
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Shinn-Forng
authors.initials: SF
authors.lastname: Peng
authors.affiliation: Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan. Electronic address: hwchiu@tmu.edu.tw.
authors.forename: Hung-Wen
authors.initials: HW
authors.lastname: Chiu
mesh_terms: D000368:Aged; D006528:Carcinoma, Hepatocellular / Q000401:mortality* / Q000601:surgery*; D017115:Catheter Ablation; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000401:mortality* / Q000601:surgery*; D008297:Male; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013624:Taiwan / Q000453:epidemiology; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome
keywords: artificial neural network; disease-free survival; hepatocellular carcinoma; medical decision support; radiofrequency ablation