Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Herng-Chia Chiu,Hong-Yaw Chen,Wen-Hsien Ho,King-Teh Lee,Te-Wei Ho

doi:10.1155/2013/201976

Abstract

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

Highlights

Hepatocellular carcinoma (HCC) is the fifth common cancer and the third leading cause of death worldwide
The significant predictors were selected according to predictive error ratio for 1, 3, and 5-year survival models using artificial neural network (ANN) and logistic regression (LR) in the order of features of demographic, clinical, surgical outcome, and
Six variables in the clinical features dimension were significant predictors in all three survival models constructed by ANN: comorbidity, liver cirrhosis, α-Fetoprotein, platelet, ASA classification, and TNM stage

Summary

Introduction

Hepatocellular carcinoma (HCC) is the fifth common cancer and the third leading cause of death worldwide. According to the World Health Organization (WHO) statistics in 2000, it has been estimated that there are at least 564,000 new cases of HCC per year around the world [1]. Though Asia and Africa have accounted for 80% of incidence cases of HCC for years, the incidence rates have been found to be significantly increasing in the United States [2] and some European nations [3]. Even with progress in diagnosis and treatment, the overall mortality in HCC patients is still higher than in other types of cancer patients. The factors associated with mortality have been explored by traditional statistical methods, such as logistic regression (LR) and Cox regression [6]. Logistic analysis models hypothesize that as mean values of a given predictor variable increase, the predicted risk of the outcome increases. Despite its recognized limitations [7], LR is still widely used in clinical outcome studies

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