Radiogenomics in Hepatocellular Carcinoma: Machine Learning Based CT Texture Analysis in Predicting β-Catenin Mutation Status

Abstract

OBJECTIVE. The purpose of this study is to evaluate the potential value of CT radiomics in predicting the mutation status of β-catenin in patients with hepatic cell cancer (HCC).MATERIALS AND METHODS.In this retrospective study, 43 patients with hepatic cell HCC (18 without β-catenin mutation and 15 with β-catenin mutation) were identified in The Cancer Genome Atlas–hepatic liver Cell Carcinoma database (TCGA-LIHC). To create stable models, the data were augmented to a total of 202 labeled samples (131 without β-catenin mutation and 73 with β-catenin mutation) by obtaining up to five different samples per patient. Extraction of large amounts of image features from portal phase contrast-enhanced CT images had been performed on an open-source software package (Pyradiomics, version 2.1.2.). Reproducibility analysis (intraclass correlation, run ICCs in SPSS 18.0) was performed by two radiologists. Classification problem is about β-catenin gene mutation status. Machine Learning based classifications were performed using the Pycaret (version 2.1.2) software. The main performance metric was the AUC value.RESULTS. Of 828 extracted texture features, 759 had excellent reproducibility. Using 10 selected features, the Extra Trees Classifier algorithm correctly classified 93.4% of the HCCs in terms of β-catenin mutation status (AUC value, 0.9741); the CatBoost Classifier algorithm correctly classified 91.9% of the HCCs (AUC value, 0.9692); Gradient Boosting Classifier algorithm correctly classified 91.1% ( AUC value, 0.9722). All the three advanced algorithms performed above 90% accuracy.CONCLUSION. Machine Learning-based high-dimensional quantitative CT radiomics analysis might be a feasible and potential method for predicting β-catenin mutation status in patients with HCC.