Ultrasound (US) imaging is part of conventional medical imaging in clinical practice that is low-cost, non-ionizing, portable and capable of real-time image acquisition and display. However, in certain cases, US has limited sensitivity and specificity in differentiating between malignant and benign lesions. Ultrasound-based radiomics, as a new branch of radiomics, can provide additional features such as heterogeneity of lesions that are invisible to the naked eye, alone or in combination with demographic, histological, genomic or proteomic data, thereby improving the accuracy of US in diagnosis of disease. This article provides an introduction to ultrasound-based radiomics, covering its workflow, the application of machine learning, and current research status. Current limitations of radiomics, such as consistency of image acquisition, parameter variations, and difficulty in calibrating quantitative methods in ultrasound, will also be covered.
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