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Abstract Details
Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review
J Clin Med. 2022 Oct 28;11(21):6368. doi: 10.3390/jcm11216368.
1Department of Surgery, University of Illinois Chicago, Chicago, IL 60607, USA.
2Faculty of Medicine, University of Aleppo, Aleppo 12212, Syria.
3Department of Internal Medicine, King George's Medical University, Lucknow 226003, Uttar Pradesh, India.
4Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain.
5Department of HPB Surgery and Liver Transplantation, BLK-MAX Superspeciality Hospital, New Delhi 110005, Delhi, India.
6Department of Surgery, Baroda Medical College and SSG Hospital, Vadodara 390001, Gujarat, India.
7Département of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1348 Brussels, Belgium.
8General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.
9Unit of Nuclear Medicine, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
10Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland.
11Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland.
12Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland.
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.