Artificial Intelligence in Imaging of Chronic Liver Diseases

Chronic liver disease.

()in: StatPearls [Internet]. StatPearls Publishing, () ()

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

World J Gastroenterol. 26: 5617-5628Zhou L.Q. Wang J.Y. Yu S.Y. et al.

Artificial intelligence in medical imaging of the liver.

World J Gastroenterol. 25: 672-682

Hepatic venous pressure gradient: clinical use in chronic liver disease.

Clin Mol Hepatol. 20: 6-14Huwart L. Peeters F. Sinkus R. et al.

Liver fibrosis: non-invasive assessment with MR elastography.

NMR Biomed. 19: 173-179Tang A. Tan J. Sun M. et al.

Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis.

Radiology. 267: 422-431Lee H. Jun D.W. Kang B.K. et al.

Estimating of hepatic fat amount using MRI proton density fat fraction in a real practice setting.

Med (United States). 96https://doi.org/10.1097/MD.0000000000007778Hu W. Yang H. Xu H. et al.

Radiomics based on artificial intelligence in liver diseases: where we are?.

Gastroenterol Rep. 8: 90-97Ronneberger O. Fischer P. Brox T.

U-net: convolutional networks for biomedical image segmentation.

Lecture Notes in computer Science (including Subseries Lecture Notes in artificial intelligence and lecture notes in bioinformatics). 9351. Springer Verlag, : 234-241

Long J, Shelhamer E, Darrell T. Fully convolutional networks for Semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015. p. 3431-40. doi: 10.1109/CVPR.2015.7298965.

Very deep convolutional networks for large-scale image recognition.

in: 3rd International conference on learning representations, ICLR 2015 - conference track Proceedings. International conference on learning representations. ICLR, San DiegoA review on support vector machine for data classification. 1. Choy G. Khalilzadeh O. Michalski M. et al.

Current applications and future impact of machine learning in radiology.

Radiology. 288: 318-328Morshid A. Elsayes K.M. Khalaf A.M. et al.

A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.

Radiol Artif Intell. 1: e180021

Spieler B, Sabottke C, Moawad AW, et al. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021. doi: 10.1007/s00261-021-03056-1. Epub ahead of print. Erratum in: Abdom Radiol (NY). 2021. PMID: 33786653.

Bilic P. Christ P.F. Vorontsov E. et al.

The liver tumor segmentation benchmark (LiTS).

arXiv. () ()

Measures of the amount of ecologic association between species.

Ecology. 26: 297-302

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool.

BMC Med Imaging. 15: 29Kavur A.E. Selver M.A. Dicle O. et al.

CHAOS - combined (CT-MR) Healthy abdominal organ segmentation challenge data.

https://doi.org/10.5281/ZENODO.3431873Ginneken B. Heimann T. Styner M.

3D segmentation in the clinic: a grand challenge. 2007.

() ()

3Dircadb | IRCAD France.

() ()

MIDAS - Collection Livers and liver tumors with expert hand segmentations.

() ()Wang K. Mamidipalli A. Retson T. et al.

Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network.

Radiol Artif Intell. 1: 180022Meine H. Chlebus G. Ghafoorian M. et al.

Comparison of U-net-based convolutional neural networks for liver segmentation in CT.

arXiv. () ()

Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

Int J Comput Assist Radiol Surg. 12: 171-182Vorontsov E. Cerny M. Régnier P. et al.

Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases.

Radiol Artif Intell. 1: 180014Ferdinand Christ P. Ezzeldin Elshaer M.A. Ettlinger F. et al.

Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields.

() ()

Ben-Cohen A, Diamant I, Klang E, et al. Fully convolutional network for liver segmentation and lesions detection. 2016.

Sun C. Guo S. Zhang H. et al.

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Artif Intell Med. 83: 58-66Ibragimov B. Toesca D. Chang D. et al.

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.

Phys Med Biol. 62: 8943-8958De Franchis R. Primignani M.

Natural history of portal hypertension in patients with cirrhosis.

Clin Liver Dis. 5: 645-663La Mura V. Abraldes J.G. Berzigotti A. et al.

Right atrial pressure is not adequate to calculate portal pressure gradient in cirrhosis: a clinical-hemodynamic correlation study.

Hepatology. 51: 2108-2116Kumar A. Khan N.M. Anikhindi S.A. et al.

Correlation of transient elastography with hepatic venous pressure gradient in patients with cirrhotic portal hypertension: a study of 326 patients from India.

World J Gastroenterol. 23: 687-696

Clinical applications of transient elastography.

Clin Mol Hepatol. 18: 163-173Sandrin L. Fourquet B. Hasquenoph J.M. et al.

Transient elastography: a new noninvasive method for assessment of hepatic fibrosis.

Ultrasound Med Biol. 29: 1705-1713Kim G. Kim M.Y. Baik S.K.

Transient elastography versus hepatic venous pressure gradient for diagnosing portal hypertension: a systematic review and meta-analysis.

Clin Mol Hepatol. 23: 34-41

Ultrasonography for noninvasive assessment of portal hypertension.

Gut Liver. 11: 464-473Kondo T. Maruyama H. Sekimoto T. et al.

Impact of portal hemodynamics on Doppler ultrasonography for predicting decompensation and long-term outcomes in patients with cirrhosis.

Scand J Gastroenterol. 51: 236-244Kim M.Y. Baik S.K. Park D.H. et al.

Damping index of Doppler hepatic vein waveform to assess the severity of portal hypertension and response to propranolol in liver cirrhosis: a prospective nonrandomized study.

Liver Int. 27: 1103-1110Smith A.D. Branch C.R. Zand K. et al.

Liver surface nodularity quantification from routine CT images as a biomarker for detection and evaluation of cirrhosis.

Radiology. 280: 771-781

Non-invasive (and minimally invasive) diagnosis of oesophageal varices.

J Hepatol. 49: 520-527

Noninvasive assessment of portal hypertension using spectral computed tomography.

J Clin Gastroenterol. 53: e387-e391Palaniyappan N. Cox E. Bradley C. et al.

Non-invasive assessment of portal hypertension using quantitative magnetic resonance imaging.

J Hepatol. 65: 1131-1139Levick C. Phillips-Hughes J. Collier J. et al.

Non-invasive assessment of portal hypertension by multi-parametric magnetic resonance imaging of the spleen: a proof of concept study.

PLoS One. 14: e0221066Lee N.K. Kim S. Kim G.H. et al.

Significance of the “Delayed hyperintense portal vein sign” in the hepatobiliary phase MRI obtained with Gd-EOB-DTPA.

J Magn Reson Imaging. 36: 678-685Asenbaum U. Ba-Ssalamah A. Mandorfer M. et al.

Effects of Portal Hypertension on Gadoxetic Acid–Enhanced Liver Magnetic Resonance.

Invest Radiol. 52: 462-469Navin P.J. Gidener T. Allen A.M. et al.

The role of magnetic resonance elastography in the diagnosis of noncirrhotic portal hypertension.

Clin Gastroenterol Hepatol. 18https://doi.org/10.1016/j.cgh.2019.10.018Wagner M. Hectors S. Bane O. et al.

Noninvasive prediction of portal pressure with MR elastography and DCE-MRI of the liver and spleen: preliminary results.

J Magn Reson Imaging. 48: 1091-1103Liu Y. Ning Z. Örmeci N. et al.

Deep convolutional neural network-aided detection of portal hypertension in patients with cirrhosis.

Clin Gastroenterol Hepatol. 18https://doi.org/10.1016/j.cgh.2020.03.034Garcia-Tsao G. Abraldes J.G. Berzigotti A. et al.

Portal hypertensive bleeding in cirrhosis: risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases.

Hepatology. 65: 310-335Ahlawat R. Hoilat G.J. Ross A.B.

Esophagogastroduodenoscopy.

StatPearls Publishing, () ()Dong T.S. Kalani A. Aby E.S. et al.

Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices.

Clin Gastroenterol Hepatol. 17: 1894-1901.e1Chalasani N. Imperiale T.F. Ismail A. et al.

Predictors of Large Esophageal Varices in Patients With Cirrhosis.

Am J Gastroenterol. 94: 3285-3291Liu F. Ning Z. Liu Y. et al.

Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study.

EBioMedicine. 36: 151-158Uno H. Cai T. Pencina M.J. et al.

On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.

Stat Med. 30: 1105-1117Zeremski M. DImova R.B. Pillardy J. et al.

Fibrosis Progression in Patients with Chronic Hepatitis C Virus Infection.

J Infect Dis. 214: 1164-1170Masugi Y. Abe T. Tsujikawa H. et al.

Quantitative assessment of liver fibrosis reveals a nonlinear association with fibrosis stage in nonalcoholic fatty liver disease.

Hepatol Commun. 2: 58-68Axley P. Mudumbi S. Sarker S. et al.

Patients with stage 3 compared to stage 4 liver fibrosis have lower frequency of and longer time to liver disease complications.

PLoS One. 13: e0197117Duarte-Rojo A. Altamirano J.T. Feld J.J.

Noninvasive markers of fibrosis: key concepts for improving accuracy in daily clinical practice.

Ann Hepatol. 11: 426-439Konerman M.A. Beste L.A. Van T. et al.

Machine learning models to predict disease progression among veterans with hepatitis C virus.

PLoS One. 14: e0208141Wei R. Wang J. Wang X. et al.

Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

EBioMedicine. 35: 124-132Wei W. Wu X. Zhou J. et al.

Noninvasive evaluation of liver fibrosis reverse using artificial neural network model for chronic hepatitis B patients.

Comput Math Methods Med. 2019https://doi.org/10.1155/2019/7239780

An algorithm for the grading of activity in chronic hepatitis C.

Hepatology. 24: 289-293Ichida F. Tsuji T. Omata M. et al.

New Inuyama classification; new criteria for histological assessment of chronic hepatitis.

Int Hepatol Commun. 6: 112-119Knodell R.G. Ishak K.G. Black W.C. et al.

Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis.

Hepatology. 1: 431-435Ishak K. Baptista A. Bianchi L. et al.

Histological grading and staging of chronic hepatitis.

J Hepatol. 22: 696-699

Classification of chronic viral hepatitis: a need for reassessment.

J Hepatol. 13: 372-374

Chronic hepatitis: an update on terminology and reporting.

Am J Surg Pathol. 19: 1409-1417Kim S.U. Oh H.J. Wanless I.R. et al.

The Laennec staging system for histological sub-classification of cirrhosis is useful for stratification of prognosis in patients with liver cirrhosis.

J Hepatol. 57: 556-563

[Histologic grading and staging of chronic hepatitis: on the basis of standardized guideline proposed by the Korean Study Group for the Pathology of Digestive Diseases].

Taehan Kan Hakhoe Chi. 9: 42-46Lee J.H. Joo I. Kang T.W. et al.

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.

Eur Radiol. 30: 1264-1273Choi K.J. Jang J.K. Lee S.S. et al.

Development and validation of a deep learning system for staging liver fibrosis by using contrast agent–enhanced CT images in the liver.

Radiology. 289: 688-697Wang K. Lu X. Zhou H. et al.

Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.

Gut. 68: 729-741Gatos I. Tsantis S. Spiliopoulos S. et al.

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

Med Phys. 46: 2298-2309Yasaka K. Akai H. Kunimatsu A. et al.

Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid–enhanced hepatobiliary phase MR images.

Radiology. 287: 146-155Park H.J. Lee S.S. Park B. et al.

Radiomics analysis of gadoxetic acid–enhanced MRI for staging liver fibrosis.

Radiology. 290: 380-387

Treacher A, Beauchamp D, Quadri B, et al. Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. doi:10.1117/12.2512592.

Brattain L.J. Telfer B.A. Dhyani M. et al.

Objective liver fibrosis estimation from shear wave elastography.

in: Proceedings of the Annual International conference of the IEEE Engineering in medicine and Biology Society, EMBS. Vol 2018-July. Institute of Electrical and Electronics Engineers Inc., : 3472-3476Mitsuka Y. Midorikawa Y. Abe H. et al.

A prediction model for the grade of liver fibrosis using magnetic resonance elastography.

BMC Gastroenterol. 17: 133Chang W. Lee J.M. Yoon J.H. et al.

Liver fibrosis staging with MR elastography: comparison of diagnostic performance between patients with chronic hepatitis B and those with other etiologic causes.

Radiology. 280: 88-97He L. Li H. Dudley J.A. et al.

Machine learning prediction of liver stiffness using clinical and T2-weighted MRI radiomic data.

Am J Roentgenol. 213: 592-601Silva A.M. Grimm R.C. Glaser K.J. et al.

Magnetic resonance elastography: evaluation of new inversion algorithm and quantitative analysis method.

Abdom Imaging. 40: 810-817Murphy M.C. Manduca A. Trzasko J.D. et al.

Artificial neural networks for stiffness estimation in magnetic resonance elastography.

Magn Reson Med. 80: 351-360Younossi Z.M. Koenig A.B. Abdelatif D. et al.

Global epidemiology of nonalcoholic fatty liver disease—meta-analytic assessment of prevalence, incidence, and outcomes.

Hepatology. 64: 73-84Ferraioli G. Monteiro L.B.S.

Ultrasound-based techniques for the diagnosis of liver steatosis.

World J Gastroenterol. 25: 6053-6062Hamaguchi M. Kojima T. Itoh Y. et al.

The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation.

Am J Gastroenterol. 102: 2708-2715Cao W. An X. Cong L. et al.

Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease.

J Ultrasound Med. 39: 51-59Park H.J. Park B. Lee S.S.

Radiomics and deep learning: Hepatic applications.

Korean J Radiol. 21: 387-401Byra M. Styczynski G. Szmigielski C. et al.

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.

Int J Comput Assist Radiol Surg. 13: 1895-1903Webb M. Yeshua H. Zelber-Sagi S. et al.

Diagnostic value of a computerized hepatorenal index for sonographic quantification of liver steatosis.

Am J Roentgenol. 192: 909-914Biswas M. Kuppili V. Edla D.R. et al.

Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.

Comput Methods Programs Biomed. 155: 165-177Saba L. Dey N. Ashour A.S. et al.

Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm.

Comput Methods Programs Biomed. 130: 118-134Acharya U.R. Sree S.V. Ribeiro R. et al.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm.

Med Phys. 39: 4255-4264Han A. Byra M. Heba E. et al.

Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks.

Radiology. 295: 342-350Mongan J. Moy L. Kahn C.E.

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers.

Radiol Artif Intell. 2: e200029Liu X. Cruz Rivera S. Moher D. et al.

CONSORT-AI extension.

Nat Med. 26: 1364-1374Cruz Rivera S. Liu X. Chan A.W. et al.

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.

Lancet Digit Heal. 2: e549-e560

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