The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer

Abstract

In this paper, to build a predictive model of hepatitis B virus (HBV) reactivation in primary liver cancer (PLC) patients after precise radiotherapy (RT). Logistic regression analysis was adopted to extract the optimal feature subset, TNM, HBV DNA level and outer margin of RT were risk factors for HBV reactivation (P < 0.05). A predictive model of support vector machine (SVM) was established for the optimal feature subset and all of PLC data sets. The experimental results proved that the former obviously improves the classification accuracy, which increased from 74.44% to 78.89%. In this paper, it is concluded that TNM, HBV DNA levels and outer boundary are the risk factor for HBV reactivation (P < 0.05).