Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Table 1 Studies applying artificial intelligence in the diagnosis of hepatocellular carcinoma

Ref.	Title	Aim of the study of the use of AI in imaging techniques	Diagnostic technique studied	AI tool used	University/department
Bharti et al[7] 2018	Preliminary study of chronic liver classification on ultrasound images using an ensemble model	Classification of liver disease in four stages; normal liver, chronic liver disease, cirrhosis and HCC	Ultrasound	CNN	Thapar Institute of Engineering & Technology, Patiala, India
Liu et al[8] 2017	Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound	Early identification of the presence of cirrhosis	Ultrasound	ML	Sun Yat-sen University, Guangzhou, China
Schmauch et al[9] 2019	Diagnosis of focal liver lesions from ultrasound using deep learning	Classify liver lesions as benign or malignant	Ultrasound	DL	Owkin Inc, Research and Development Laboratory, Paris, France
Guo et al[10] 2018	A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images	Characterize liver lesions and identify data of malignancy	C-US	ML	University School of Medicine, Shanghai, China
Mokrane et al[13] 2020	Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules	Identify malignancy in hepatic space-occupying lesions catalogued as indeterminate	CT	Radiomics	Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, United States
Yasaka et al[14] 2018	Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: A preliminary study	Classification of liver lesions in five categories	CT	CNN	Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
Vivanti et al[15] 2017	Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies	Detection of tumor recurrence analyzing volume/tumor load	CT	CNN	The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Li et al[16] 2015	Automatic segmentation of liver tumor in CT Images with deep convolutional neural networks	Liver tumor segmentation	CT	CNN	Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Hamm et al[17] 2019	Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI	Classification of liver lesions	MRI	DL	Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States
Jansen et al[18] 2019	Automatic classification of focal liver lesions based on MRI and risk factors	Classification of liver lesions in: Adenomas, cysts, hemangiomas, HCC and metastasis	MRI	ML	Image Sciences Institute, University Medical Center Utrecht & Utrecht University, Utrecht, the Netherlands
Zhang et al[19] 2018	Liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework	Classification of liver tissue	MRI	CNN	Department of Biomedical Engineering, Yale University, New Haven, CT, United States
Preis et al[20] 2011	Neural network evaluation of pet scans of the liver: A potentially useful adjunct in clinical interpretation	Identify metastatic liver disease	PET	CNN	Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston
Kiani et al[21] 2020	Impact of a deep learning assistant on the histopathologic classification of liver cancer	Differentiate HCC from cholangiocarcinoma	Histology	DL	Department of Computer Science, Stanford University, Stanford, CA, United States
Liao et al[22] 2020	Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma	Automated identification of liver tumor tissue, differentiating it from healthy tissue	Histology	DL	Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China

Note: AI: Artificial intelligence; HCC: Hepatocellular carcinoma; CNN: Convolutional neural network; ML: Machine learning; DL: Deep learning; C-US: Contrast-enhanced ultrasound; CT: Computed tomography; MRI: Magnetic resonance imaging; PET: Positron emission tomography.