Cutoff Values of Acoustic Radiation Force Impulse Two-Location Measurements in Different Etiologies of Liver Fibrosis

Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort: High liver cancer, low comorbidity in hepatitis B virus

BACKGROUND

Acoustic radiation force impulse (ARFI) is used to measure liver fibrosis and predict outcomes. The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus (HBV) than in other etiologies of chronic liver disease.

AIM

To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.

METHODS

Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled. After excluding dual infection, alcoholism, autoimmune hepatitis, and others with incomplete data, this retrospective cohort were divided into hepatitis B (HBV, n = 1064), hepatitis C (HCV, n = 507), and non-HBV, non-HCV (NBNC, n = 391) groups. The indexed cases were linked to cancer registration (1987-2020) and national mortality databases. The differences in morbidity and mortality among the groups were analyzed.

RESULTS

At the enrollment, the HBV group showed more males (77.5%), a higher prevalence of pre-diagnosed hepatocellular carcinoma (HCC), and a lower prevalence of comorbidities than the other groups (P < 0.001). The HCV group was older and had a lower platelet count and higher ARFI score than the other groups (P < 0.001). The NBNC group showed a higher body mass index and platelet count, a higher prevalence of pre-diagnosed non-HCC cancers (P < 0.001), especially breast cancer, and a lower prevalence of cirrhosis. Male gender, ARFI score, and HBV were independent predictors of HCC. The 5-year risk of HCC was 5.9% and 9.8% for those ARFI-graded with severe fibrosis and cirrhosis. ARFI alone had an area under the receiver operating characteristic curve (AUROC) of 0.742 for prediction of HCC in 5 years. AUROC increased to 0.828 after adding etiology, gender, age, and platelet score. No difference was found in mortality rate among the groups.

CONCLUSION

The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups. Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning

BACKGROUND

Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.

AIM

To develop a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images.

METHODS

Using multi-view ultrasound data from 3310 patients, 19513 studies, and 228075 images from a retrospective cohort of patients received elastography, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from clinical ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis.

RESULTS

The DL algorithm demonstrated repeatable measurements with a moderate number of images (three for each viewpoint) and high agreement across three premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: Areas under the curve of the ROC to classify mild, moderate, and severe steatosis grades were 0.85, 0.91, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter (CAP) with statistically significant improvements for all levels on the unblinded histology-proven cohort and for “= severe” steatosis on the blinded histology-proven cohort.

CONCLUSION

The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than the CAP.

Comparison of 2-D Shear Wave Elastography and Point Shear Wave Elastography for Assessing Liver Fibrosis

Progressive liver fibrosis may result in cirrhosis, portal hypertension and increased risk of hepatocellular carcinoma. We performed a meta-analysis to compare liver fibrosis staging in chronic liver disease patients using 2-D shear wave elastography (2-D SWE) and point shear wave elastography (pSWE). The PubMed, Web of Science and Cochrane Library databases were searched until May 31, 2020 for studies evaluating the diagnostic performance of 2-D SWE and pSWE in assessing liver fibrosis. Pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios and area under receiver operating characteristic curve were estimated using the bivariate random effects model. As a result, 71 studies with 11,345 patients were included in the analysis. The pooled sensitivities of 2-D SWE and pSWE significantly differed for the detection of significant fibrosis (F ≥ 2; 0.84 vs. 0.76, p < 0.001) and advanced fibrosis (F ≥ 3; 0.90 vs. 0.83, p = 0.003), but not for detection of cirrhosis (F = 4; 0.89 vs. 0.85, p = 0.090). The pooled specificities of 2-D SWE and pSWE did not significantly differ for detection of F ≥ 2 (0.81 vs. 0.79, p = 0.753), F ≥ 3 (0.87 vs. 0.83, p = 0.163) or F = 4 (0.87 vs. 0.84, p = 0.294). Both 2-D SWE and pSWE have high sensitivity and specificity for detecting each stage of liver fibrosis. Two-dimensional SWE has higher sensitivity than pSWE for detection of significant fibrosis and advanced fibrosis.