{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:43:41Z","timestamp":1793277821729,"version":"4.5.0"},"reference-count":111,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA206180"],"award-info":[{"award-number":["R01CA206180"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004820","name":"Connecticut Department of Energy and Environmental Protection","doi-asserted-by":"publisher","award":["ZMVI1-2520DAT111"],"award-info":[{"award-number":["ZMVI1-2520DAT111"]}],"id":[{"id":"10.13039\/100004820","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006098","name":"Radiological Society of North America","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006098","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013043","name":"Yale Center for Clinical Investigation, Yale School of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013043","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["jhep-reports.eu","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["JHEP Reports"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1016\/j.jhepr.2022.100443","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T10:28:02Z","timestamp":1643797682000},"page":"100443","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":197,"title":["Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction"],"prefix":"10.1016","volume":"4","author":[{"given":"David","family":"Nam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Chapiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3142-3762","authenticated-orcid":false,"given":"Valerie","family":"Paradis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9884-8752","authenticated-orcid":false,"given":"Tobias Paul","family":"Seraphin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5348","authenticated-orcid":false,"given":"Jakob Nikolas","family":"Kather","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jhepr.2022.100443_bib1","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1097\/00042737-200309000-00004","article-title":"Inter-observer and intra-observer variability in hepatology","volume":"15","author":"Winkfield","year":"2003","journal-title":"Eur J Gastroenterol Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib2","series-title":"Artificial intelligence: a modern approach","author":"Russell","year":"2002"},{"key":"10.1016\/j.jhepr.2022.100443_bib3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1159\/000090032","article-title":"Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein","volume":"6","author":"Pearce","year":"2006","journal-title":"Pancreatology"},{"key":"10.1016\/j.jhepr.2022.100443_bib4","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat Commun"},{"key":"10.1016\/j.jhepr.2022.100443_bib5","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.cgh.2009.09.031","article-title":"Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines","volume":"8","author":"Waljee","year":"2010","journal-title":"Clin Gastroenterol Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib6","doi-asserted-by":"crossref","first-page":"e295","DOI":"10.1016\/S2589-7500(20)30063-7","article-title":"A\u00a0deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations","volume":"2","author":"Sabanayagam","year":"2020","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.jhepr.2022.100443_bib7","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A\u00a0clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Toma\u0161ev","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.jhepr.2022.100443_bib8","first-page":"4","article-title":"Natural language processing in medicine: a review","volume":"38","author":"Locke","year":"2021","journal-title":"Trends Anaesth Crit Care"},{"key":"10.1016\/j.jhepr.2022.100443_bib9","article-title":"Benchmarking artificial intelligence methods for end-to-end computational pathology","author":"Laleh","year":"2021","journal-title":"bioRxiv"},{"key":"10.1016\/j.jhepr.2022.100443_bib10","first-page":"1","article-title":"Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis","author":"Fu","year":"2020","journal-title":"Nat Cancer"},{"key":"10.1016\/j.jhepr.2022.100443_bib11","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ejca.2021.08.039","article-title":"Deep learning can predict lymph node status directly from histology in colorectal cancer","volume":"157","author":"Kiehl","year":"2021","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.jhepr.2022.100443_bib12","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s00330-020-07230-9","article-title":"Applications of artificial intelligence (AI) in diagnostic radiology: a technography study","volume":"31","author":"Rezazade Mehrizi","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jhep.2018.09.014","article-title":"Burden of liver diseases in the world","volume":"70","author":"Asrani","year":"2019","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib14","doi-asserted-by":"crossref","first-page":"101894","DOI":"10.1016\/j.compmedimag.2021.101894","article-title":"An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer","volume":"89","author":"He","year":"2021","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.jhepr.2022.100443_bib15","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1038\/s41746-020-00324-0","article-title":"The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database","volume":"3","author":"Benjamens","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib16","doi-asserted-by":"crossref","DOI":"10.1038\/s41568-020-00327-9","article-title":"Designing deep learning studies in cancer diagnostics","author":"Kleppe","year":"2021","journal-title":"Nat Rev Cancer"},{"key":"10.1016\/j.jhepr.2022.100443_bib17","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1038\/s41437-020-0303-2","article-title":"Big data in digital healthcare: lessons learnt and recommendations for general practice","volume":"124","author":"Agrawal","year":"2020","journal-title":"Heredity"},{"key":"10.1016\/j.jhepr.2022.100443_bib18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1159\/000443344","article-title":"EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease","volume":"9","year":"2016","journal-title":"Obes Facts"},{"key":"10.1016\/j.jhepr.2022.100443_bib19","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1136\/gutjnl-2020-322880","article-title":"Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers","volume":"70","author":"Calderaro","year":"2021","journal-title":"Gut"},{"key":"10.1016\/j.jhepr.2022.100443_bib20","doi-asserted-by":"crossref","first-page":"105614","DOI":"10.1016\/j.cmpb.2020.105614","article-title":"Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology","volume":"195","author":"Teramoto","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.jhepr.2022.100443_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/s21061993","article-title":"Efficiency of machine learning algorithms for the determination of macrovesicular steatosis in frozen sections stained with Sudan to evaluate the quality of the graft in liver transplantation","volume":"21","author":"P\u00e9rez-Sanz","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.jhepr.2022.100443_bib22","doi-asserted-by":"crossref","first-page":"106153","DOI":"10.1016\/j.cmpb.2021.106153","article-title":"Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides","volume":"207","author":"Qu","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.jhepr.2022.100443_bib23","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.humpath.2013.11.011","article-title":"Automatic classification of white regions in liver biopsies by supervised machine learning","volume":"45","author":"Vanderbeck","year":"2014","journal-title":"Hum Pathol"},{"key":"10.1016\/j.jhepr.2022.100443_bib24","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.cgh.2019.12.025","article-title":"High-throughput, machine learning-based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease","volume":"18","author":"Forlano","year":"2020","journal-title":"Clin Gastroenterol Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib25","article-title":"An improved qFibrosis algorithm for precise screening and enrollment into non-alcoholic steatohepatitis (NASH) clinical trials","volume":"10","author":"Leow","year":"2020","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.jhepr.2022.100443_bib26","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1038\/s41374-020-0463-y","article-title":"Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies","volume":"100","author":"Roy","year":"2020","journal-title":"Lab Invest"},{"key":"10.1016\/j.jhepr.2022.100443_bib27","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/hep.31750","article-title":"A\u00a0machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH","volume":"74","author":"Taylor-Weiner","year":"2021","journal-title":"Hepatology"},{"key":"10.1016\/j.jhepr.2022.100443_bib28","doi-asserted-by":"crossref","first-page":"151518","DOI":"10.1016\/j.anndiagpath.2020.151518","article-title":"Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD","volume":"47","author":"Gawrieh","year":"2020","journal-title":"Ann Diagn Pathol"},{"key":"10.1016\/j.jhepr.2022.100443_bib29","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1007\/s11548-021-02410-4","article-title":"LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images","volume":"16","author":"Aatresh","year":"2021","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.jhepr.2022.100443_bib30","doi-asserted-by":"crossref","first-page":"11579","DOI":"10.1038\/s41598-021-90444-8","article-title":"A\u00a0generalized deep learning framework for whole-slide image segmentation and analysis","volume":"11","author":"Khened","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.jhepr.2022.100443_bib31","doi-asserted-by":"crossref","first-page":"101914","DOI":"10.1016\/j.media.2020.101914","article-title":"A\u00a0hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images","volume":"68","author":"Wang","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jhepr.2022.100443_bib32","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.compbiomed.2017.03.017","article-title":"Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading","volume":"84","author":"Li","year":"2017","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib33","doi-asserted-by":"crossref","first-page":"104075","DOI":"10.1016\/j.compbiomed.2020.104075","article-title":"NucleiSegNet: robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images","volume":"128","author":"Lal","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib34","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/cyto.a.23871","article-title":"Classification and\u00a0segmentation of hyperspectral data of hepatocellular carcinoma samples using 1-D convolutional neural network","volume":"97","author":"Wang","year":"2020","journal-title":"Cytometry A"},{"key":"10.1016\/j.jhepr.2022.100443_bib35","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1109\/JBHI.2019.2949837","article-title":"Deep learning-based classification of liver cancer histopathology images using only global labels","volume":"24","author":"Sun","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.jhepr.2022.100443_bib36","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1038\/s41598-020-80610-9","article-title":"Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images","volume":"11","author":"Roy","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.jhepr.2022.100443_bib37","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41746-020-0232-8","article-title":"Impact of a deep learning assistant on the histopathologic classification of liver cancer","volume":"3","author":"Kiani","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib38","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1245\/s10434-019-08190-1","article-title":"Classification and prognosis prediction from histopathological images of hepatocellular carcinoma by a fully automated pipeline based on machine learning","volume":"27","author":"Liao","year":"2020","journal-title":"Ann Surg Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib39","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41379-020-00671-z","article-title":"Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning","volume":"34","author":"Saito","year":"2021","journal-title":"Mod Pathol"},{"key":"10.1016\/j.jhepr.2022.100443_bib40","doi-asserted-by":"crossref","DOI":"10.1002\/hep.31207","article-title":"Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides","author":"Saillard","year":"2020","journal-title":"Hepatology"},{"key":"10.1016\/j.jhepr.2022.100443_bib41","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1136\/gutjnl-2020-320930","article-title":"Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning","volume":"70","author":"Shi","year":"2021","journal-title":"Gut"},{"key":"10.1016\/j.jhepr.2022.100443_bib42","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1038\/s41598-021-81506-y","article-title":"Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images","volume":"11","author":"Yamashita","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.jhepr.2022.100443_bib43","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1038\/s41575-020-0343-3","article-title":"Development of AI-based pathology biomarkers in gastrointestinal and liver cancer","volume":"17","author":"Kather","year":"2020","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib44","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib45","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1053\/j.gastro.2020.06.021","article-title":"Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning","volume":"159","author":"Echle","year":"2020","journal-title":"Gastroenterology"},{"key":"10.1016\/j.jhepr.2022.100443_bib46","article-title":"Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study","volume":"0","author":"Muti","year":"2021","journal-title":"Lancet Digital Health"},{"key":"10.1016\/j.jhepr.2022.100443_bib47","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1038\/s43018-020-0087-6","article-title":"Pan-cancer image-based detection of clinically actionable genetic alterations","volume":"1","author":"Kather","year":"2020","journal-title":"Nat Cancer"},{"key":"10.1016\/j.jhepr.2022.100443_bib48","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1038\/s41467-021-24698-1","article-title":"The impact of site-specific digital histology signatures on deep learning model accuracy and bias","volume":"12","author":"Howard","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.jhepr.2022.100443_bib49","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1016\/j.jhep.2020.06.025","article-title":"Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials","volume":"73","author":"Davison","year":"2020","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib50","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1002\/jmri.26839","article-title":"User and system pitfalls in liver imaging with LI-RADS","volume":"50","author":"Elsayes","year":"2019","journal-title":"J\u00a0Magn Reson Imaging"},{"key":"10.1016\/j.jhepr.2022.100443_bib51","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.3390\/cancers13071673","article-title":"Overview of prognostic systems for hepatocellular carcinoma and ITA.LI.CA external validation of MESH and CNLC classifications","volume":"13","author":"Vitale","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.jhepr.2022.100443_bib52","article-title":"Systematic review and meta-analysis of validated\u00a0prognostic models for resected hepatocellular carcinoma patients","author":"Beumer","year":"2021","journal-title":"Eur J Surg Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib53","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1038\/nrgastro.2014.67","article-title":"Have we finally found the ultimate staging system for HCC?","volume":"11","author":"Chapiro","year":"2014","journal-title":"Nat Rev Gastroenterol Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib54","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jhep.2018.03.019","article-title":"EASL clinical practice guidelines: management of hepatocellular carcinoma","volume":"69","author":"Galle","year":"2018","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib55","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.jhep.2021.07.004","article-title":"Systemic treatment of hepatocellular carcinoma: an EASL position paper","volume":"75","author":"Bruix","year":"2021","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib56","doi-asserted-by":"crossref","first-page":"3625","DOI":"10.1007\/s00261-021-03100-0","article-title":"Imaging of treatment response during systemic therapy for hepatocellular carcinoma","volume":"46","author":"Masch","year":"2021","journal-title":"Abdom Radiol (NY)"},{"key":"10.1016\/j.jhepr.2022.100443_bib57","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.jhep.2021.05.025","article-title":"EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis \u2013 2021 update","volume":"75","author":"Berzigotti","year":"2021","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"van Timmeren","year":"2020","journal-title":"Insights Imaging"},{"key":"10.1016\/j.jhepr.2022.100443_bib59","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1148\/rg.2017170077","article-title":"Deep learning: a primer for radiologists","volume":"37","author":"Chartrand","year":"2017","journal-title":"Radiographics"},{"key":"10.1016\/j.jhepr.2022.100443_bib60","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Lecture Notes Comp Sci"},{"key":"10.1016\/j.jhepr.2022.100443_bib61","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","first-page":"415","article-title":"Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields","author":"Christ","year":"2016"},{"key":"10.1016\/j.jhepr.2022.100443_bib62","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.artmed.2017.03.008","article-title":"Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs","volume":"83","author":"Sun","year":"2017","journal-title":"Artif Intell Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib63","series-title":"The Liver Tumor Segmentation Benchmark (LiTS). arXiv [csCV]","author":"Bilic","year":"2019"},{"key":"10.1016\/j.jhepr.2022.100443_bib64","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.jhepr.2022.100443_bib65","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/s00261-020-02604-5","article-title":"Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning","volume":"46","author":"Bousabarah","year":"2021","journal-title":"Abdom Radiol (NY)"},{"key":"10.1016\/j.jhepr.2022.100443_bib66","doi-asserted-by":"crossref","first-page":"669437","DOI":"10.3389\/fonc.2021.669437","article-title":"Advanced deep learning approach to automatically segment malignant tumors and ablation zone in the liver with contrast-enhanced CT","volume":"11","author":"He","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib67","doi-asserted-by":"crossref","first-page":"102041","DOI":"10.1016\/j.media.2021.102041","article-title":"Anatomy-guided multimodal registration by learning segmentation without ground truth: application to intraprocedural CBCT\/MR liver segmentation and registration","volume":"71","author":"Zhou","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jhepr.2022.100443_bib68","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","article-title":"Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm","volume":"155","author":"Biswas","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.jhepr.2022.100443_bib69","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s11548-018-1843-2","article-title":"Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images","volume":"13","author":"Byra","year":"2018","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.jhepr.2022.100443_bib70","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1148\/radiol.2019190512","article-title":"Automated liver fat quantification at nonenhanced abdominal CT for population-based steatosis assessment","volume":"293","author":"Graffy","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib71","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1148\/radiol.2020191160","article-title":"Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks","volume":"295","author":"Han","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib72","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1136\/gutjnl-2018-316204","article-title":"Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"10.1016\/j.jhepr.2022.100443_bib73","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1148\/radiol.2018180763","article-title":"Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver","volume":"289","author":"Choi","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib74","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1148\/radiol.2017171928","article-title":"Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid\u2013enhanced hepatobiliary phase MR images","volume":"287","author":"Yasaka","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib75","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1148\/radiol.2017170706","article-title":"Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study","volume":"286","author":"Yasaka","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib76","doi-asserted-by":"crossref","first-page":"3338","DOI":"10.1007\/s00330-019-06205-9","article-title":"Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI","volume":"29","author":"Hamm","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib77","doi-asserted-by":"crossref","first-page":"4981","DOI":"10.1007\/s00330-020-07559-1","article-title":"Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver","volume":"31","author":"Oestmann","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib78","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5152\/dir.2018.17467","article-title":"A\u00a0radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma","volume":"24","author":"Peng","year":"2018","journal-title":"Diagn Interv Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib79","doi-asserted-by":"crossref","first-page":"3595","DOI":"10.1007\/s00330-018-5985-y","article-title":"Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT","volume":"29","author":"Ma","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib80","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.jhep.2019.02.023","article-title":"Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma","volume":"70","author":"Xu","year":"2019","journal-title":"J\u00a0Hepatol"},{"key":"10.1016\/j.jhepr.2022.100443_bib81","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1007\/s00330-018-5935-8","article-title":"Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI","volume":"29","author":"Feng","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib82","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s00432-020-03366-9","article-title":"Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning","volume":"147","author":"Jiang","year":"2021","journal-title":"J\u00a0Cancer Res Clin Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib83","doi-asserted-by":"crossref","DOI":"10.1007\/s00432-021-03617-3","article-title":"Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters","author":"Song","year":"2021","journal-title":"J\u00a0Cancer Res Clin Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib84","doi-asserted-by":"crossref","first-page":"588010","DOI":"10.3389\/fonc.2021.588010","article-title":"Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks","volume":"11","author":"Zhou","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib85","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.jvir.2018.01.769","article-title":"Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept","volume":"29","author":"Abajian","year":"2018","journal-title":"J\u00a0Vasc Interv Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib86","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2019180021","article-title":"A\u00a0machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization","volume":"1","author":"Morshid","year":"2019","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.jhepr.2022.100443_bib87","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00330-019-06318-1","article-title":"Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging","volume":"30","author":"Peng","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib88","doi-asserted-by":"crossref","first-page":"101034","DOI":"10.1016\/j.tranon.2021.101034","article-title":"Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: a multicenter study","volume":"14","author":"Jin","year":"2021","journal-title":"Transl Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib89","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s11547-020-01174-2","article-title":"CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners","volume":"125","author":"Hu","year":"2020","journal-title":"Radiol Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib90","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1148\/radiol.2020192224","article-title":"Preparing medical imaging data for machine learning","volume":"295","author":"Willemink","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib91","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.jhepr.2022.100443_bib92","unstructured":"Profiles n.d. http:\/\/qibawiki.rsna.org\/index.php\/Profiles (accessed December 19, 2021)."},{"key":"10.1016\/j.jhepr.2022.100443_bib93","unstructured":"Biomarkers inventory. European Society of Radiology n.d. https:\/\/www.myesr.org\/research\/biomarkers-inventory (accessed December 19, 2021)."},{"key":"10.1016\/j.jhepr.2022.100443_bib94","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1097\/RLI.0000000000000666","article-title":"Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence","volume":"55","author":"Hagiwara","year":"2020","journal-title":"Invest Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib95","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib96","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/bjc.2014.639","article-title":"Transparent reporting\u00a0of\u00a0a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"112","author":"Collins","year":"2015","journal-title":"Br J Cancer"},{"key":"10.1016\/j.jhepr.2022.100443_bib97","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib98","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1093\/jamia\/ocaa094","article-title":"Latent bias and the implementation of artificial intelligence in medicine","volume":"27","author":"DeCamp","year":"2020","journal-title":"J\u00a0Am Med Inform Assoc"},{"key":"10.1016\/j.jhepr.2022.100443_bib99","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1148\/radiol.2019192515","article-title":"Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers\u2014from the radiology editorial board","volume":"294","author":"Bluemke","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.jhepr.2022.100443_bib100","doi-asserted-by":"crossref","first-page":"E14","DOI":"10.1038\/s41586-020-2766-y","article-title":"Massive Analysis Quality Control (MAQC) Society Board of Directors, Waldron L, et\u00a0al. Transparency and reproducibility in artificial intelligence","volume":"586","author":"Haibe-Kains","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.jhepr.2022.100443_bib101","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(19)30037-6","article-title":"Reporting of artificial intelligence prediction models","volume":"393","author":"Collins","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.jhepr.2022.100443_bib102","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.jhepr.2022.100443_bib103","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1038\/s41591-020-0941-1","article-title":"Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group","volume":"26","author":"Sounderajah","year":"2020","journal-title":"Nat Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib104","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1038\/s41591-019-0603-3","article-title":"Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed","volume":"25","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib105","doi-asserted-by":"crossref","first-page":"3786","DOI":"10.1007\/s00330-020-07684-x","article-title":"To buy or not to buy\u2014evaluating commercial AI solutions in radiology (the ECLAIR guidelines)","volume":"31","author":"Omoumi","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib106","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190043","article-title":"On the interpretability of artificial intelligence in radiology: challenges and opportunities","volume":"2","author":"Reyes","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.jhepr.2022.100443_bib107","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1007\/s00330-019-06214-8","article-title":"Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features","volume":"29","author":"Wang","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jhepr.2022.100443_bib108","doi-asserted-by":"crossref","first-page":"680","DOI":"10.3389\/fonc.2020.00680","article-title":"Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data","volume":"10","author":"Zhen","year":"2020","journal-title":"Front Oncol"},{"key":"10.1016\/j.jhepr.2022.100443_bib109","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ejmp.2021.02.013","article-title":"A\u00a0deep survival interpretable radiomics model of hepatocellular carcinoma patients","volume":"82","author":"Wei","year":"2021","journal-title":"Phys Med"},{"key":"10.1016\/j.jhepr.2022.100443_bib110","doi-asserted-by":"crossref","DOI":"10.23915\/distill.00030","article-title":"Multimodal neurons in artificial neural networks","volume":"6","author":"Goh","year":"2021","journal-title":"Distill"},{"key":"10.1016\/j.jhepr.2022.100443_bib111","series-title":"CLIP: Connecting Text and Images","author":"Radford","year":"2021"}],"container-title":["JHEP Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589555922000155?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589555922000155?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T22:28:29Z","timestamp":1762900109000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2589555922000155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":111,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["S2589555922000155"],"URL":"https:\/\/doi.org\/10.1016\/j.jhepr.2022.100443","relation":{},"ISSN":["2589-5559"],"issn-type":[{"value":"2589-5559","type":"print"}],"subject":[],"published":{"date-parts":[[2022,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction","name":"articletitle","label":"Article Title"},{"value":"JHEP Reports","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jhepr.2022.100443","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).","name":"copyright","label":"Copyright"}],"article-number":"100443"}}