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

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.