Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 8 March 2023 / Approved: 13 March 2023 / Online: 13 March 2023 (02:13:50 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

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. Because of organ shortages this treatment can only be applied to carefully selected patients according to clinical guidelines to minimize risk of recurrence. 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 like segmentation can be automated and workflow in radiology departments can be improved. Other roles include in depth pixel-wise analysis of lesions by radiomics or deep learning in order to find new imaging criteria that allow better prediction of treatment response or risk of recurrence. Liver transplant is an ideal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease and artificial intelligence 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

Subject

Medicine and Pharmacology, Gastroenterology and Hepatology

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