Deep learning for noninvasive liver fibrosis classification: A systematic review.
deep learning
diagnostic imaging
liver fibrosis
neural networks (computer)
Journal
Liver international : official journal of the International Association for the Study of the Liver
ISSN: 1478-3231
Titre abrégé: Liver Int
Pays: United States
ID NLM: 101160857
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
revised:
23
04
2021
received:
23
03
2021
accepted:
13
05
2021
pubmed:
20
5
2021
medline:
3
11
2021
entrez:
19
5
2021
Statut:
ppublish
Résumé
While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool. Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.
Sections du résumé
BACKGROUND AND AIMS
While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging.
METHODS
Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool.
RESULTS
Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability.
CONCLUSIONS
Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.
Types de publication
Journal Article
Review
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2269-2278Informations de copyright
© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Terrault NA, Lok AS, McMahon BJ, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560-1599.
Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. Hepatology. 1996;24(2):289-293.
Bravo AA, Sheth SG, Chopra S. Liver biopsy. N Engl J Med. 2001;344(7):495-500.
Rockey DC, Bissell DM. Noninvasive measures of liver fibrosis. Hepatology. 2006;43(Suppl 1):S113-S120.
Castera L. Noninvasive methods to assess liver disease in patients with hepatitis B or C. Gastroenterology. 2012;142(6):1293-1302.e1294.
Kim BK, Kim SA, Park YN, et al. Noninvasive models to predict liver cirrhosis in patients with chronic hepatitis B. Liver Int. 2007;27(7):969-976.
Patel K, Sebastiani G. Limitations of non-invasive tests for assessment of liver fibrosis. JHEP Rep. 2020;2(2):100067.
Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019;290(3):590-606.
Klang E. Deep learning and medical imaging. J Thorac Dis. 2018;10(3):1325-1328.
Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H. Fully convolutional network for liver segmentation and lesions detection. In: Carneiro G, ed. Deep learning and data labeling for medical applications. Berlin, Germany: Springer; 2016:77-85.
Wei J, Jiang H, Gu D, et al. Radiomics in liver diseases: current progress and future opportunities. Liver Int. 2020;40(9):2050-2063.
Giordano S, Takeda S, Donadon M, et al. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence. Liver Int. 2020;40(12):3117-3124.
Moher D, Liberati A, Tetzlaff J, Altman DG, Group PRISMA. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264-269.
Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med. 2011;155(8):529-536.
Kwong MT, Colopy GW, Weber AM, Ercole A, Bergmann JH. The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: a systematic review. Bio-Des Manuf. 2019;2(1):31-40.
Islam MS, Hasan MM, Wang X, Germack HD. A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare. 2018;6(2):54.
Anteby R, Horesh N, Soffer S, et al. Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis. Surg Endosc. 2021;35(4):1521-1533.
Moola S, Munn Z, Tufanaru C, et al. Chapter 7: Systematic reviews of etiology and risk In: Aromataris E, Munn Z, eds. Joanna Briggs Institute Reviewer's Manual. Adelaide, Australia: The Joanna Briggs Institute (University of Adelaide); 2017.
Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016;18(12):e323.
Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689.
Kagadis GC, Drazinos P, Gatos I, et al. Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences. Phys Med Biol. 2020;65(21):215027.
Zhou H, Wang K, Tian J, Ruiter NV, Byram BC. The accurate non-invasive staging of liver fibrosis using deep learning radiomics based on transfer learning of shear wave elastography. In: Medical Imaging 2020: Ultrasonic Imaging and Tomography. International Society for Optics and Photonics; 2020. https://doi.org/10.1117/12.2549425
Lee JH, Joo I, Kang TW, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30(2):1264-1273.
Xue L-Y, Jiang Z-Y, Fu T-T, et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis. Eur Radiol. 2020;30(5):2973-2983.
Gatos 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. 2019;46(5):2298-2309.
Brattain LJ, Telfer BA, Dhyani M, Grajo JR, Samir AE. Objective liver fibrosis estimation from shear wave elastography. Paper presented at: 2018 40th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 18-21 July 2018.
Li Y, He Q, Luo J. A deep learning trial on transient elastography for assessment of liver fibrosis. Paper presented at: 2018 IEEE International Ultrasonics Symposium (IUS); 22-25 October 2018.
Dandan L, Huanhuan M, Xiang L, Yu J, Jing J, Yi S. Classification of diffuse liver diseases based on ultrasound images with multimodal features. Paper presented at: 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 20-23 May 2019.
Wang 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. 2019;68(4):729-741.
Meng D, Zhang L, Cao G, Cao W, Zhang G, Hu B. Liver fibrosis classification based on transfer learning and FCNet for ultrasound images. IEEE Access. 2017;5:5804-5810.
Hectors SJ, Kennedy P, Huang KH, et al. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI. Eur Radiol. 2021;31(6):3805-3814.
Huang Y, Chen Y, Zhu H, et al. A liver fibrosis staging method using cross-contrast network. Expert Syst Appl. 2019;130:124-131.
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR Images. Radiology. 2018;287(1):146-155.
Li Q, Yu B, Tian X, Cui X, Zhang R, Guo Q. Deep residual nets model for staging liver fibrosis on plain CT images. Int J Comput Assist Radiol Surg. 2020;15(8):1399-1406.
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018;28(11):4578-4585.
Choi KJ, Jang JK, Lee SS, 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. 2018;289(3):688-697.
Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology: promises and pitfalls. Am J Gastroenterol. 2019;114(3):422-428.
Wang NC, Zhang P, Tapper EB, Saini S, Wang SC, Su GL. Automated measurements of muscle mass using deep learning can predict clinical outcomes in patients with liver disease. Am J Gastroenterol. 2020;115(8):1210-1216.
Koulaouzidis A, Iakovidis DK, Yung DE, et al. KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes. Endosc Int Open. 2017;5(6):E477-E483.
Mooney P. Retinal OCT Images (optical coherence tomography). 2018. https://www.kaggle.com/paultimothymooney/kermany2018. Accessed May 24, 2021.
Kaggle. Kaggle: Your Machine Learning and Data Science Community. 2020. https://www.kaggle.com/. Accessed May 24, 2021.
Graham B. Kaggle Diabetic Retinopathy Detection Competition Report. Coventary, UK: University of Warwick; 2015.