A new method for predicting the microvascular invasion status of hepatocellular carcinoma through neural network analysis

Abstract

Abstract Aims To determine the relationship between microvascular invasion (MVI) and the clinical features of hepatocellular carcinoma (HCC) and provide a method to evaluate MVI status by neutral network analysis. Methods The patients were divided into two groups (MVI-positive group and MVI-negative group). Univariate analysis and multivariate logistic regression analysis were carried out to identify the independent risk factors for MVI positivity. Neural network analysis was used to analyze the different importance of the risk factors in MVI prediction. Results We enrolled 1697 patients in this study. We found that the independent prognostic factors were age, NEU, multiple tumors, AFP level and tumor diameter. By neural network analysis, we proposed that the level of AFP was the most important risk factor for HCC in predicting MVI status (the AUC was 0.704). However, age was the most important risk factor for early-stage HCC with a single tumor (the AUC was 0.605). Conclusion Through the neutral network analysis, we could conclude that the level of AFP is the most important risk factor for MVI-positive patients and the age is the most important risk factor for early-stage HCC with a single tumor.