Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods
Journal
Computer Methods and Programs in Biomedicine
Journal Volume
117
Journal Issue
3
Pages
425
Date Issued
2014-12-01
Author(s)
Abstract
Background and 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. ? 2014 Elsevier Ireland Ltd.
Subjects
Feature selection; Hepatocellular carcinoma; Predictive model; Radiofrequency ablation; Support vector machine
SDGs
Other Subjects
Ablation; Decision trees; Diagnosis; Feature extraction; Genetic algorithms; Patient treatment; Simulated annealing; Support vector machines; Tumors; Feature selection methods; Hepatocellular carcinoma; Hybrid feature selections; Negative predictive value; Positive predictive values; Predictive modeling; Radio-frequency Ablation; Simulated annealing algorithms; Predictive analytics; alanine aminotransferase; alpha fetoprotein; aspartate aminotransferase; chlorotrianisene; accuracy; adult; aged; alanine aminotransferase blood level; algorithm; Article; artificial embolism; cancer palliative therapy; cancer patient; cancer prognosis; cancer recurrence; cancer size; cancer staging; clinical feature; computer assisted tomography; controlled study; female; follow up; genetic algorithm; human; liver cell carcinoma; liver cirrhosis; major clinical study; male; multimodality cancer therapy; nuclear magnetic resonance imaging; postoperative period; prediction; predictive value; process model; protein blood level; radiofrequency ablation; radiofrequency ablation device; random forest; recurrence predictive model; recurrence risk; risk assessment; sensitivity and specificity; sex difference; simulated annealing algorithm; support vector machine; validation process; Carcinoma, Hepatocellular; catheter ablation; Liver Neoplasms; medical informatics; middle aged; procedures; radiofrequency radiation; receiver operating characteristic; recurrent disease; reproducibility; theoretical model; Aged; Carcinoma, Hepatocellular; Catheter Ablation; Female; Humans; Liver Neoplasms; Male; Medical Informatics; Middle Aged; Models, Theoretical; Predictive Value of Tests; Radio Waves; Recurrence; Reproducibility of Results; ROC Curve; Sensitivity and Specificity; Support Vector Machines
Publisher
Elsevier Ireland Ltd
Type
journal article