The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

SIMPLE SUMMARY: As a primary hepatic tumor, hepatocellular carcinoma (HCC) is the most prevalent kind. Recent developments in magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to enhance detection, segmentation, discrimination from HCC mimics, and tracking of therapy response. Radiomics, artificial intelligence (AI), and related methods have already been used with promising results in other diagnostic imaging fields. In this review, we briefly cover the radiomics and AI clinical applications for the identification, segmentation, and management of HCC. In addition, we also look into their potential to improve HCC diagnosis and provide appropriate treatment planning. Finally, overall limitations and future directions of the research in this field are outlined. ABSTRACT: Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.