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Review
. 2022 Dec 12;14(24):6123.
doi: 10.3390/cancers14246123.

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

Dalia Fahmy  1 , Ahmed Alksas  2 , Ahmed Elnakib  2 , Ali Mahmoud  2 , Heba Kandil  2   3 , Ashraf Khalil  4 , Mohammed Ghazal  5 , Eric van Bogaert  6 , Sohail Contractor  6 , Ayman El-Baz  2
Affiliations
Review

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

Dalia Fahmy et al. Cancers (Basel). .

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.

Keywords: AI; computed tomography; deep learning; hepatocellular carcinoma; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A summary of different applications of Artificial Intelligence and Radiomics in the field of HCC.
Figure 2
Figure 2
An illustration of the different types of radiomics and the steps involved.
See this image and copyright information in PMC

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This research received no external funding.

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