Photon-counting Detector CT for Liver Fat Quantification: Validation across Protocols in Metabolic Dysfunction-associated Steatotic Liver Disease

Lipid Microparticle-Based Phantoms Modeling Hepatic Steatosis for the Validation of Quantitative Imaging Techniques

Metabolic dysfunction-associated steatotic liver disease (MASLD) typically presents as "macrovesicular steatosis", where each hepatocyte contains a large fat vacuole (30-50 µm), indicating a more indolent form. In about 20% of cases, "microvesicular steatosis" occurs, with smaller vacuoles (1-15 µm) linked to steatohepatitis, cirrhosis progression, and increased risk of liver cancer. Emerging quantitative ultrasound (QUS) liver fat quantification (QUS-LFQ) tools measure various acoustic properties, but few methods compare techniques and imaging modalities, and the impact of fat vacuole size remains unclear. This study introduces a methodology to create ultrasound (US) phantoms that replicate fat vesicle size in MASLD. While imaging phantoms validate quantitative tools, no model currently links QUS-LFQ measurements to steatosis severity. Existing homogeneous phantoms assessing properties like attenuation, backscatter, and speed of sound overlook the microstructure of steatosis, despite the known effect of particle size on acoustic interactions. Here, agar-based phantoms simulate fat accumulation in steatotic hepatocytes using stable peanut oil droplets as analogs for lipid vacuoles. Microscopy and sizing confirm stability at 4 °C, 23 °C, and 50 °C. Both microscopy and US imaging confirm uniform distribution, with QUS-LFQ measurements reflecting fat content. These phantoms hold promise for validating quantitative imaging methods, particularly for US-based MASLD screening tools.

AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA)

INTRODUCTION

About one-third of adults in the USA have some grade of hepatic steatosis. Coronary artery calcium (CAC) scans contain more information than currently reported. We previously reported new artificial intelligence (AI) algorithms applied to CAC scans for opportunistic measurement of bone mineral density, cardiac chamber volumes, left ventricular mass, and other imaging biomarkers collectively referred to as AI-cardiovascular disease (CVD). In this study, we investigate a new AI-CVD algorithm for opportunistic measurement of liver steatosis.

METHODS

We applied AI-CVD to CAC scans from 5702 asymptomatic individuals (52% female, age 62±10 years) in the Multi-Ethnic Study of Atherosclerosis. Liver attenuation index (LAI) was measured using the percentage of voxels below 40 Hounsfield units. We used Cox proportional hazards regression to examine the association of LAI with incident CVD and mortality over 15 years, adjusted for CVD risk factors and the Agatston CAC score.

RESULTS

A total of 751 CVD and 1343 deaths accrued over 15 years. Mean±SD LAI in females and males was 38±15% and 43±13%, respectively. Participants in the highest versus lowest quartile of LAI had greater incidence of CVD over 15 years: 19% (95% CI 17% to 22%) vs 12% (10% to 14%), respectively, p<0.0001. Individuals in the highest quartile of LAI (Q4) had a higher risk of CVD (HR 1.43, 95% CI 1.08 to 1.89), stroke (HR 1.77, 95% CI 1.09 to 2.88), and all-cause mortality (HR 1.36, 95% CI 1.10 to 1.67) compared with those in the lowest quartile (Q1), independent of CVD risk factors.

CONCLUSION

AI-enabled liver steatosis measurement in CAC scans provides opportunistic and actionable information for early detection of individuals at elevated risk of CVD events and mortality, without additional radiation.

New Evidence, Creative Insights, and Strategic Solutions: Advancing the Understanding and Practice of Diabetes Osteoporosis

BACKGROUND

Diabetes osteoporosis is a debilitating condition that significantly impacts human health. However, it is often underdiagnosed and not addressed in a timely or appropriate manner.

METHODS

Recent studies were reviewed to explore the roles of energy metabolism, sarcopeina, low-grade inflammation and gut microbiota in the development of diabetes osteoporosis.

RESULTS

Osteoporosis in diabetic patients differs from primary osteoporosis. Novel biomarkers and risk factors that are biologically, physiologically, and pathologically linked to the development of diabetes osteoporosis are emerging, necessitating a shift in strategies for diagnosis, risk stratification, and prevention of diabetes osteoporosis.

CONCLUSIONS

There is an urgent need to approach this disorder from a fresh perspective, initiating a range of basic research and clinical investigations.