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Volume 13
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Review

Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

by
Mihai Dan Pomohaci
1,2,
Mugur Cristian Grasu
1,2,*,
Radu Lucian Dumitru
1,2,
Mihai Toma
1,2 and
Ioana Gabriela Lupescu
1,2
1
Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
2
Department of Radiology, The University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(9), 1663; https://doi.org/10.3390/diagnostics13091663
Submission received: 8 March 2023 / Revised: 26 April 2023 / Accepted: 5 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Advances in the Diagnosis and Management of Liver Disease and Liver Transplantation)
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Abstract

Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. However, due to organ shortages, this treatment can only be applied to carefully selected patients according to clinical guidelines. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis. Our objectives are to review the developing AI applications that could be implemented to better stratify liver transplant candidates. The papers analysed applied AI for liver segmentation, evaluation of steatosis, sarcopenia assessment, lesion detection, segmentation, and characterization. A liver transplant is an optimal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease. Furthermore, AI could provide solutions for improving the management of liver transplant candidates to improve survival.
Keywords: hepatocarcinoma; cirrhosis; liver transplantation; liver transplant; artificial intelligence; machine learning; radiomics; deep learning; neural networks hepatocarcinoma; cirrhosis; liver transplantation; liver transplant; artificial intelligence; machine learning; radiomics; deep learning; neural networks

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MDPI and ACS Style

Pomohaci, M.D.; Grasu, M.C.; Dumitru, R.L.; Toma, M.; Lupescu, I.G. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics 2023, 13, 1663. https://doi.org/10.3390/diagnostics13091663

AMA Style

Pomohaci MD, Grasu MC, Dumitru RL, Toma M, Lupescu IG. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics. 2023; 13(9):1663. https://doi.org/10.3390/diagnostics13091663

Chicago/Turabian Style

Pomohaci, Mihai Dan, Mugur Cristian Grasu, Radu Lucian Dumitru, Mihai Toma, and Ioana Gabriela Lupescu. 2023. "Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence" Diagnostics 13, no. 9: 1663. https://doi.org/10.3390/diagnostics13091663

APA Style

Pomohaci, M. D., Grasu, M. C., Dumitru, R. L., Toma, M., & Lupescu, I. G. (2023). Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics, 13(9), 1663. https://doi.org/10.3390/diagnostics13091663

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