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.

Table 2 Studies applying artificial intelligence for the treatment of hepatocellular carcinoma

Ref.	Tittle	n	Study type	Study aim	AI tool used	Outcome	University/department
Dong et al[30] 2020	Preoperative prediction of microvascular invasion in hepatocellular carcinoma: initial application of a radiomic algorithm based on grayscale ultrasound images	322	Retrospective	Prediction of VMI using C-US	Radiomics	AUC: 0.73; Sen: 0.919; Spe: 0.359	Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
Xu et al[28] 2019	Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma	495	Retrospective	Prediction of VMI using C-CT	Radiomics	AUC: 0.90	Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu Province, China
Ma et al[27] 2019	Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT	157	Retrospective	Prediction of VMI using C-CT	Radiomics	AUC: 0.73	Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China
Zhou et al[29] 2017	Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images	46	Retrospective	Prediction of VMI using C-MRI		AUC: 0.918; Sen: 92%; Spe: 66%	Laboratory for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Ziv et al[46] 2017	Gene signature associated with upregulation of the Wnt/β-Catenin signaling pathway predicts tumor response to transarterial embolization	17	Retrospective	Prediction of response to TACE using signature gene		Prediction accuracy: 70%	Interventional Radiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
Morshid et al[38] 2019	A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization	105	Retrospective	Prediction of response to TACE using CT	ML	Acc: 74%	Departments of Imaging Physics, Diagnostic Radiology, Gastrointestinal Oncology and Interventional Radiology, The University of Texas, MD Anderson Cancer Center, Houston
Liu et al[40] 2020	Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound	130	Retrospective	Prediction of response to TACE using C-US	DL	AUC: 0.93	Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, China
Peng et al[39] 2020	Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging	789	Retrospective	Prediction of response to TACE using CT	CNN	AUC: 0.97; Acc: 84.3%	Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, China
Abajian et al[41] 2018	Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept	36	Retrospective	Prediction of response to TACE using MRI	ML	Acc: 78%; Sen: 62%; Spe: 82%	Yale School of Medicine, Department of Radiology and Biomedical Imaging, United States
Mähringer-Kunz et al[42] 2020	Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A pilot study	282	Retrospective	Prediction of survival after TACE	CNN	Acc: 0.77; Sen: 78%; Spe: 81%	Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
Saillard et al[35] 2020	Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides	194	Retrospective	Prediction of survival after surgical resection	DL	C-index: 0.78	Owkin Lab, Owkin
Liang et al[47] 2014	Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods	83	Prospective	Prediction of recurrence after RFA	ML	AUC: 67%; Sen: 86%; Spe: 82%	Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
Ji et al[31] 2019	Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study	470	Retrospective	Prediction of recurrence after resection	ML	C-index: 0.633-0.699	Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China

Note: All studies were retrospective studies in their design, except the study by Liang et al[47] was prospective. AI: Artificial intelligence; CNN: Convolutional neural network; ML: Machine learning; DL: Deep learning; VMI: Vascular microinvasion; C-US: Contrast-enhanced ultrasound; AUC: Area under the curve; Acc: Accuracy; Sen: Sensitivity; Spe: Specificity; CT: Computed tomography; C-CT: Contrast-enhanced CT; MRI: Magnetic resonance imaging; C-MRI: Contrast-enhanced MRI; TACE: Transcatheter arterial chemoembolization; RFA: Radiofrequency ablation.