TY - JOUR
T1 - Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks
AU - Han, Aiguo
AU - Byra, Michal
AU - Heba, Elhamy
AU - Andre, Michael P.
AU - Erdman, John W.
AU - Loomba, Rohit
AU - Sirlin, Claude B.
AU - O'Brien, William D.
N1 - Funding Information:
Author contributions: Guarantors of integrity of entire study, A.H., W.D.O.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, A.H., M.P.A., R.L., C.B.S.; clinical studies, A.H., E.H., M.P.A., R.L., C.B.S.; experimental studies, A.H., M.P.A., W.D.O.; statistical analysis, A.H., M.B., W.D.O.; and manuscript editing, all authors Disclosures of Conflicts of Interest: A.H. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received funding from Siemens Healthineers for the prior study. Other relationships: disclosed no relevant relationships. M.B. disclosed no relevant relationships. E.H. disclosed no relevant relationships. M.P.A. disclosed no relevant relationships. J.W.E. disclosed no relevant relationships. R.L. Activities related to the present article: received funding from NIEHS (5P42ES010337), NCATS (5UL1TR001442), NIDDK (R01DK106419, P30DK120515), and DOD PR-CRP (CA170674P2); received an investigator-initiated study grant from Siemens. Activities not related to the present article: disclosed no relevant relationships. Other relationships: is a consultant or advisory board member for Arrowhead Pharmaceuticals, AstraZeneca, Bird Rock Bio, Boehringer Ingelheim, Bristol- Myer Squibb, Celgene, Cirius, CohBar, Conatus, Eli Lilly, Galmed, Gemphire, Gilead, Glympse bio, GNI, GRI Bio, Intercept, Ionis, Janssen Inc., Merck, Meta-crine, NGM Biopharmaceuticals, Novartis, Novo Nordisk, Pfizer, Prometheus, Sanofi, Siemens, and Viking Therapeutics; institution has received grant support from Allergan, Boehringer-Ingelheim, Bristol-Myers Squibb, Cirius, Eli Lilly, Galectin Therapeutics, Galmed Pharmaceuticals, GE, Genfit, Gilead, Intercept, Grail, Janssen, Madrigal Pharmaceuticals, Merck, NGM Biopharmaceuticals, NuSirt, Pfizer, pH Pharma, Prometheus, and Siemens; is the co-founder of Li-ponexus. C.B.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is on the board of the Society of Abdominal Radiology, AMRA, Guerbet, and Bristol Myers Squibb; is a consultant for GE Healthcare, Bayer, AMRA, Fulcrum Therapeutics, and IBM/ Watson Health; institution received grants from Gilead, GE Healthcare, Siemens, GE MRI, Bayer, GE Digital, GE US, ACR Innovation, Philips, and Celgene; is a speaker for GE Healthcare and Bayer; institution receives royalties from Wolt-ers Kluwer Health (UpToDate Publishing); developed educational presentations for Medscape and Resoundant; institution has lab service agreements with En-anta, ICON Medical Imaging, Gilead, Shire, Virtualscopics, Intercept, Synageva, Takeda, Genzyme, Janssen, NuSirt, Celgene-Parexel, and Organovo; has independent consulting contracts with Epigenomics and Blade Therapeutics; developed educational presentations or articles for Medscape. Other relationships: disclosed no relevant relationships. W.D.O. disclosed no relevant relationships.
Funding Information:
From the Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering (A.H., W.D.O.), and Department of Food Science and Human Nutrition (J.W.E.), University of Illinois at Urbana-Champaign, 306 N Wright St, Urbana, IL 61801; Department of Radiology (M.B., M.P.A.), Liver Imaging Group, Department of Radiology (E.H., C.B.S.), and NAFLD Research Center, Division of Gastroenterology, Department of Medicine (R.L.), University of California, San Diego, La Jolla, Calif; and Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (M.B.). Received May 21, 2019; revision requested July 29; revision received December 2; accepted December 18. Address correspondence to A.H. (e-mail: han51@illinois.edu). Supported by the National Institutes of Health (R01DK106419). 1Current address: Department of Radiology, SUNY Upstate Medical University, Syracuse, NY. Conflicts of interest are listed at the end of this article. See also the editorial by Lockhart and Smith in this issue.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Background: Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose: To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods: A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results: A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years 6 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years 6 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion: Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded.
AB - Background: Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose: To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods: A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results: A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years 6 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years 6 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion: Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded.
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U2 - 10.1148/radiol.2020191160
DO - 10.1148/radiol.2020191160
M3 - Article
C2 - 32096706
AN - SCOPUS:85084205600
VL - 295
SP - 342
EP - 350
JO - Radiology
JF - Radiology
SN - 0033-8419
IS - 2
ER -