Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics

Abstract

To establish an ultrasound-based radiomics model through machine learning methods and then to assess the ability of the model to differentiate infected focal liver lesions from malignant mimickers. A total of 104 patients with infected focal liver lesions and 485 patients with malignant hepatic tumours were included, consisting of hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), combined hepatocellular-cholangiocarcinoma (cHCC-CC), and liver metastasis. Radiomics features were extracted from grey-scale ultrasound images. Feature selection and predictive modelling were carried out by dimensionality reduction methods and classifiers. The diagnostic effect of the prediction mode was assessed by receiver operating characteristic (ROC) curve analysis. In total, 5,234 radiomics features were extracted from grey-scale ultrasound image of every focal liver lesion. The ultrasound-based radiomics model had a favourable predictive value for differentiating infected focal liver lesions from malignant hepatic tumours, with an area under the curve (AUC) of 0.887 and 0.836 (HCC group), 0.896 and 0.766 (CC group), 0.944 and 0.754 (cHCC-CC group), 0.918 and 0.808 (liver metastasis group), and 0.949 and 0.745 (malignant hepatic tumour group) for the training set and validation set, respectively. Ultrasound-based radiomics is helpful in differentiating infected focal liver lesions from malignant mimickers and has the potential for use as a supplement to conventional grey-scale ultrasound and contrast-enhanced ultrasound (CEUS).