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

Dalia Fahmy; Ahmed Alksas; Ahmed Elnakib; Ali Mahmoud; Heba Kandil; Ashraf Khalil; Mohammed Ghazal; Eric van Bogaert; Sohail Contractor; Ayman El-Baz

doi:10.3390/cancers14246123

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

Dalia Fahmy, Ahmed Alksas, Ahmed Elnakib, Ali Mahmoud, Heba Kandil, Ashraf Khalil, Mohammed Ghazal, Eric van Bogaert, Sohail Contractor, Ayman El-Baz

School of Engineering (Behrend)

Research output: Contribution to journal › Review article › peer-review

13 Scopus citations

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.

Original language	English (US)
Article number	6123
Journal	Cancers
Volume	14
Issue number	24
DOIs	https://doi.org/10.3390/cancers14246123
State	Published - Dec 2022

All Science Journal Classification (ASJC) codes

Oncology
Cancer Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/cancers14246123

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

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.",

author = "Dalia Fahmy and Ahmed Alksas and Ahmed Elnakib and Ali Mahmoud and Heba Kandil and Ashraf Khalil and Mohammed Ghazal and {van Bogaert}, Eric and Sohail Contractor and Ayman El-Baz",

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language = "English (US)",

volume = "14",

journal = "Cancers",

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T1 - The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

AU - Fahmy, Dalia

AU - Alksas, Ahmed

AU - Elnakib, Ahmed

AU - Mahmoud, Ali

AU - Kandil, Heba

AU - Khalil, Ashraf

AU - Ghazal, Mohammed

AU - van Bogaert, Eric

AU - Contractor, Sohail

AU - El-Baz, Ayman

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

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U2 - 10.3390/cancers14246123

DO - 10.3390/cancers14246123

M3 - Review article

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JO - Cancers

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

Abstract

All Science Journal Classification (ASJC) codes

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