Copyright ©The Author(s) 2020.
World J Gastroenterol. Oct 7, 2020; 26(37): 5617-5628
Published online Oct 7, 2020. doi: 10.3748/wjg.v26.i37.5617
Table 1 Studies applying artificial intelligence in the diagnosis of hepatocellular carcinoma
Ref.TitleAim of the study of the use of AI in imaging techniquesDiagnostic technique studiedAI tool usedUniversity/department
Bharti et al[7] 2018Preliminary study of chronic liver classification on ultrasound images using an ensemble modelClassification of liver disease in four stages; normal liver, chronic liver disease, cirrhosis and HCCUltrasoundCNNThapar Institute of Engineering & Technology, Patiala, India
Liu et al[8] 2017Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasoundEarly identification of the presence of cirrhosisUltrasoundMLSun Yat-sen University, Guangzhou, China
Schmauch et al[9] 2019Diagnosis of focal liver lesions from ultrasound using deep learningClassify liver lesions as benign or malignantUltrasoundDLOwkin Inc, Research and Development Laboratory, Paris, France
Guo et al[10] 2018A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound imagesCharacterize liver lesions and identify data of malignancyC-USMLUniversity School of Medicine, Shanghai, China
Mokrane et al[13] 2020Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodulesIdentify malignancy in hepatic space-occupying lesions catalogued as indeterminateCTRadiomicsDepartment of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, United States
Yasaka et al[14] 2018Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: A preliminary studyClassification of liver lesions in five categoriesCTCNNDepartment of Radiology, The University of Tokyo Hospital, Tokyo, Japan
Vivanti et al[15] 2017Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studiesDetection of tumor recurrence analyzing volume/tumor loadCTCNNThe Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Li et al[16] 2015Automatic segmentation of liver tumor in CT Images with deep convolutional neural networksLiver tumor segmentationCTCNNResearch Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Hamm et al[17] 2019Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRIClassification of liver lesionsMRIDLDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, United States
Jansen et al[18] 2019Automatic classification of focal liver lesions based on MRI and risk factorsClassification of liver lesions in: Adenomas, cysts, hemangiomas, HCC and metastasisMRIMLImage Sciences Institute, University Medical Center Utrecht & Utrecht University, Utrecht, the Netherlands
Zhang et al[19] 2018Liver tissue classification using an auto-context-based deep neural network with a multi-phase training frameworkClassification of liver tissueMRICNNDepartment of Biomedical Engineering, Yale University, New Haven, CT, United States
Preis et al[20] 2011Neural network evaluation of pet scans of the liver: A potentially useful adjunct in clinical interpretationIdentify metastatic liver diseasePETCNNDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston
Kiani et al[21] 2020Impact of a deep learning assistant on the histopathologic classification of liver cancerDifferentiate HCC from cholangiocarcinomaHistologyDLDepartment of Computer Science, Stanford University, Stanford, CA, United States
Liao et al[22] 2020Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinomaAutomated identification of liver tumor tissue, differentiating it from healthy tissueHistologyDLDepartment 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
Table 2 Studies applying artificial intelligence for the treatment of hepatocellular carcinoma
Ref.TittlenStudy typeStudy aimAI tool usedOutcomeUniversity/department
Dong et al[30] 2020Preoperative prediction of microvascular invasion in hepatocellular carcinoma: initial application of a radiomic algorithm based on grayscale ultrasound images322RetrospectivePrediction of VMI using C-USRadiomicsAUC: 0.73; Sen: 0.919; Spe: 0.359Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
Xu et al[28] 2019Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma495RetrospectivePrediction of VMI using C-CTRadiomicsAUC: 0.90Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu Province, China
Ma et al[27] 2019Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT157RetrospectivePrediction of VMI using C-CTRadiomicsAUC: 0.73Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China
Zhou et al[29] 2017Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images46RetrospectivePrediction of VMI using C-MRIAUC: 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] 2017Gene signature associated with upregulation of the Wnt/β-Catenin signaling pathway predicts tumor response to transarterial embolization17RetrospectivePrediction of response to TACE using signature genePrediction accuracy: 70%Interventional Radiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
Morshid et al[38] 2019A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization105RetrospectivePrediction of response to TACE using CTMLAcc: 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] 2020Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound130RetrospectivePrediction of response to TACE using C-USDLAUC: 0.93Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, China
Peng et al[39] 2020Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging789RetrospectivePrediction of response to TACE using CTCNNAUC: 0.97; Acc: 84.3%Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, China
Abajian et al[41] 2018Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept36RetrospectivePrediction of response to TACE using MRIMLAcc: 78%; Sen: 62%; Spe: 82%Yale School of Medicine, Department of Radiology and Biomedical Imaging, United States
Mähringer-Kunz et al[42] 2020Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A pilot study282RetrospectivePrediction of survival after TACECNNAcc: 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] 2020Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides194RetrospectivePrediction of survival after surgical resectionDLC-index: 0.78Owkin Lab, Owkin
Liang et al[47] 2014Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods83ProspectivePrediction of recurrence after RFAMLAUC: 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] 2019Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study470RetrospectivePrediction of recurrence after resectionMLC-index: 0.633-0.699Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China