CT texture analysis in predicting treatment response and survival in patients with hepatocellular carcinoma treated with transarterial chemoembolization

Abstract

Background Using texture features derived from contrast-enhanced computed tomography (CT) combined with general imaging features as well as clinical information to predict treatment response and survival in patients with hepatocellular carcinoma (HCC) who received transarterial chemoembolization (TACE) treatment. Methods From January 2014 to June 2020, 301 patients with HCC who underwent TACE were retrospectively reviewed. Their treatment-naïve contrast-enhanced CTs were retrieved and reviewed by two independent radiologists. Four general imaging features were evaluated, including the largest lesion diameter, the number of lesions, the presence or absence of portal vein thrombus and the presence or absence of ascites. Texture features were extracted based on the regions of interest (ROIs) drawn on the slice with the largest axial diameter of all lesions using Pyradiomics. After excluding features with low reproducibility, the remaining features with high prognostic value were selected for further analyses. The data were randomly divided in a ratio of 8:2 for model training and testing. A random forest classifier was built to predict treatment response. Random survival forest and Cox proportional-hazards models were constructed to predict patients’ overall (OS) and progress-free survival (PFS). Results We retrospectively evaluated 301 patients (average 55.3 years old) with HCC treated with TACE. After feature reduction and selection, 22 features were included in model construction. The random forest classifier including texture features achieved an area under the curve (AUC) of 0.968 with an accuracy of 88.3% for predicting treatment response, significantly higher than the model without texture features. Along with important clinical and general image features, texture features are significantly correlated with overall and progress-free survival, especially for Gray-level size zone matrix (GLSZM) group (p < 0.05). Conclusions Random forest algorithm based on texture features combined with general imaging features, and clinical information is a robust method for predicting prognosis in patients with HCC treated with TACE, which may help avoid additional examinations and assist in treatment planning.