Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.

INTRODUCTION: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. MATERIALS AND METHODS: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes. RESULTS: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). CONCLUSIONS: The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

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.

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

BACKGROUND: Acoustic radiation force impulse (ARFI) imaging is a popular modality to measure liver fibrosis. ARFI selects optimal locations for measurement under imaging guiding. However, there are concerns on study locations and observers bias. To decrease the variations, ARFI at two locations was measured with standardized protocol. This study attempted to establish its cutoff values according to Metavir fibrosis score in different etiologies. METHODS: A consecutive series of patients who received liver histology study were prospectively enrolled. All cases had hemogram, liver biochemistry, viral markers, and ARFI two-location measurements within 4 weeks of histology study. A standardized protocol was performed by single technologist. We excluded patients with alanine aminotransferase >5x upper limit normal. RESULTS: Five hundred and ten patients that included 153 seronegative for both HBsAg and anti-HCV Non-B non-C (NBNC), 33 autoimmune liver diseases (AILD), 261 chronic hepatitis B (CHB), and 63 chronic hepatitis C (CHC) were enrolled. About 83% of NBNC patients had fat cell >5%. For diagnosis of liver cirrhosis, the area under receiver operating characteristic curve of NBNC, AILD, CHB, and CHC groups was 0.937, 0.929, 0.784, and 0.937; the cutoff values for mean ARFI were 1.788, 2.095, 1.455, and 1.710 m/s, respectively. The sensitivity and specificity are both over 0.818 for patients with nonalcoholic fatty liver diseases, CHC, and AILD, but the corresponding data are only 0.727-0.756 in CHB. The Fibrosis-4 Score is as good as ARFI on fibrosis assessment in NBNC. CONCLUSION: The performance of ARFI two-location measurement is excellent in NBNC, AILD, and CHC, but is only satisfactory in CHB.