Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks

Abstract

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 ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 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. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.

Graphical abstract

Figure 1:

Flowchart of study participants included and excluded in the study, adapted from reference . PDFF = proton density fat fraction.

Figure 2a:

Data from 22-year-old woman with low proton density fat fraction (1%) (control participant, denoted participant A). Computer-reconstructed nonenhanced ultrasound B-mode images (sagittal plane with time gain compensation) and the underlying radiofrequency signals. (a) B-mode image frame 1 (with time gain compensation), with yellow outline superimposed to indicate the region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to the blue line in a, without and with time gain compensation. (c) B-mode image frame 2 (with time gain compensation). (d) Radiofrequency signals corresponding to same location as indicated by the blue line in c but different frames (blue = frame 1, black = frame 2) without and with time gain compensation. Fixed region of interest includes signals from outside the liver.

Figure 2b:

Data from 22-year-old woman with low proton density fat fraction (1%) (control participant, denoted participant A). Computer-reconstructed nonenhanced ultrasound B-mode images (sagittal plane with time gain compensation) and the underlying radiofrequency signals. (a) B-mode image frame 1 (with time gain compensation), with yellow outline superimposed to indicate the region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to the blue line in a, without and with time gain compensation. (c) B-mode image frame 2 (with time gain compensation). (d) Radiofrequency signals corresponding to same location as indicated by the blue line in c but different frames (blue = frame 1, black = frame 2) without and with time gain compensation. Fixed region of interest includes signals from outside the liver.

Figure 2c:

Data from 22-year-old woman with low proton density fat fraction (1%) (control participant, denoted participant A). Computer-reconstructed nonenhanced ultrasound B-mode images (sagittal plane with time gain compensation) and the underlying radiofrequency signals. (a) B-mode image frame 1 (with time gain compensation), with yellow outline superimposed to indicate the region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to the blue line in a, without and with time gain compensation. (c) B-mode image frame 2 (with time gain compensation). (d) Radiofrequency signals corresponding to same location as indicated by the blue line in c but different frames (blue = frame 1, black = frame 2) without and with time gain compensation. Fixed region of interest includes signals from outside the liver.

Figure 2d:

Data from 22-year-old woman with low proton density fat fraction (1%) (control participant, denoted participant A). Computer-reconstructed nonenhanced ultrasound B-mode images (sagittal plane with time gain compensation) and the underlying radiofrequency signals. (a) B-mode image frame 1 (with time gain compensation), with yellow outline superimposed to indicate the region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to the blue line in a, without and with time gain compensation. (c) B-mode image frame 2 (with time gain compensation). (d) Radiofrequency signals corresponding to same location as indicated by the blue line in c but different frames (blue = frame 1, black = frame 2) without and with time gain compensation. Fixed region of interest includes signals from outside the liver.

Figure 3a:

Data from 50-year-old man with high proton density fat fraction (28%) (participant with nonalcoholic fatty liver disease, denoted participant B). Computer-reconstructed nonenhanced ultrasound B-mode image (transverse plane with time gain compensation) and underlying radiofrequency signals. (a) B-mode image frame 1 for participant B, with yellow outline superimposed to indicate region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to blue dashed line shown in a, without and with time gain compensation. Boundaries of the liver are not well delineated, and it is unclear whether the fixed region of interest includes signals from outside the liver.

Figure 3b:

Data from 50-year-old man with high proton density fat fraction (28%) (participant with nonalcoholic fatty liver disease, denoted participant B). Computer-reconstructed nonenhanced ultrasound B-mode image (transverse plane with time gain compensation) and underlying radiofrequency signals. (a) B-mode image frame 1 for participant B, with yellow outline superimposed to indicate region of interest for deep learning analysis. (b) Radiofrequency signals corresponding to blue dashed line shown in a, without and with time gain compensation. Boundaries of the liver are not well delineated, and it is unclear whether the fixed region of interest includes signals from outside the liver.

Figure 4a:

Receiver operating characteristic curves with 95% confidence bands of the composite nonalcoholic fatty liver disease classification scores yielded by the classifier for the test group using radiofrequency ultrasound signals (a) without and (b) with time gain compensation as the inputs. AUC = area under receiver operating characteristic curve.

Figure 4b:

Receiver operating characteristic curves with 95% confidence bands of the composite nonalcoholic fatty liver disease classification scores yielded by the classifier for the test group using radiofrequency ultrasound signals (a) without and (b) with time gain compensation as the inputs. AUC = area under receiver operating characteristic curve.

Figure 5a:

Predicted fat fraction versus MRI-derived proton density fat fraction obtained by using radiofrequency signals (a) without and (b) with time gain compensation. Blue lines represent the linear range. Gray line represents the identity line.

Figure 5b:

Predicted fat fraction versus MRI-derived proton density fat fraction obtained by using radiofrequency signals (a) without and (b) with time gain compensation. Blue lines represent the linear range. Gray line represents the identity line.

Figure 6a:

Difference between predicted fat fraction (FF) and MRI-derived proton density fat fraction (PDFF) versus the MRI-derived PDFF plots obtained by using radiofrequency signals (a) without and (b) with time gain compensation. SD = standard deviation.

Figure 6b:

Difference between predicted fat fraction (FF) and MRI-derived proton density fat fraction (PDFF) versus the MRI-derived PDFF plots obtained by using radiofrequency signals (a) without and (b) with time gain compensation. SD = standard deviation.