Document Type
Article
Source of Publication
Cancers
Publication Date
12-1-2022
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.
DOI Link
ISSN
Publisher
MDPI AG
Volume
14
Issue
24
Disciplines
Computer Sciences
Keywords
AI, computed tomography, deep learning, hepatocellular carcinoma, machine learning
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
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.
https://zuscholars.zu.ac.ae/works/5555
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series