Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation
Journal
Journal of the Formosan Medical Association
Journal Volume
116
Journal Issue
10
Pages
765-773
Date Issued
2017
Author(s)
Abstract
Background/Purpose 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). Conclusion 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. ? 2017
SDGs
Other Subjects
alanine aminotransferase; aspartate aminotransferase; aged; alanine aminotransferase blood level; Article; artificial neural network; aspartate aminotransferase blood level; cancer patient; cancer prognosis; cancer surgery; cancer survival; chemoembolization; clinical decision support system; computer assisted tomography; diagnostic accuracy; disease free survival; female; human; intermethod comparison; internal validity; liver cell carcinoma; major clinical study; male; medical record review; perceptron; predictive value; prospective study; radiofrequency ablation; receiver operating characteristic; retrospective study; sensitivity and specificity; simulation; survival prediction; catheter ablation; disease free survival; epidemiology; liver cell carcinoma; liver tumor; mortality; Taiwan; treatment outcome; validation study; x-ray computed tomography; Aged; Carcinoma, Hepatocellular; Catheter Ablation; Disease-Free Survival; Female; Humans; Liver Neoplasms; Male; Neural Networks (Computer); Retrospective Studies; Sensitivity and Specificity; Taiwan; Tomography, X-Ray Computed; Treatment Outcome
Type
journal article