Source of Publication
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.
AI, computed tomography, deep learning, hepatocellular carcinoma, machine learning
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Fahmy, Dalia; Alksas, Ahmed; Elnakib, Ahmed; Mahmoud, Ali; Kandil, Heba; Khalil, Ashraf; Ghazal, Mohammed; van Bogaert, Eric; Contractor, Sohail; and El-Baz, Ayman, "The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma" (2022). All Works. 5555.
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Gold: This publication is openly available in an open access journal/series