A fully automated deep learning-based CT liver fat quantification tool demonstrated excellent agreement with manual measurements (mean difference 2.7 HU, r2=0.92) and identified a >50% prevalence of at least mild hepatic steatosis in an asymptomatic screening cohort.
Cohort (n=9,552)
No
Does a deep learning-based automated liver fat quantification tool accurately measure hepatic steatosis on nonenhanced abdominal CT compared to manual measurement in asymptomatic adults?
A fully automated deep learning tool for nonenhanced CT accurately quantifies liver fat, demonstrating that over 50% of an asymptomatic screening cohort had at least mild steatosis.
Estimación del efecto: r2 = 0.92
Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning–based automated liver fat quantification tool at nonenhanced CT for establishing the prevalence of steatosis in a large screening cohort. Materials and Methods In this retrospective study, a fully automated liver segmentation algorithm was applied to noncontrast abdominal CT examinations from consecutive asymptomatic adults by using three-dimensional convolutional neural networks, including a subcohort with follow-up scans. Automated volume-based liver attenuation was analyzed, including conversion to CT fat fraction, and compared with manual measurement in a large subset of scans. Results A total of 11 669 CT scans in 9552 adults (mean age ± standard deviation, 57.2 years ± 7.9; 5314 women and 4238 men; median body mass index BMI, 27.8 kg/m2) were evaluated, including 2117 follow-up scans in 1862 adults (mean age, 59.2 years; 971 women and 891 men; mean interval, 5.5 years). Algorithm failure occurred in seven scans. Mean CT liver attenuation was 55 HU ± 10, corresponding to CT fat fraction of 6.4% (slightly fattier in men than in women [7.4% ± 6.0 vs 5.8% ± 5.7%; P 28%). Excellent agreement was seen between automated and manual measurements, with a mean difference of 2.7 HU (median, 3 HU) and r2 of 0.92. Among the subcohort with longitudinal follow-up, mean change was only −3 HU ± 9, but 43.3% (806 of 1861) of patients changed steatosis category between first and last scans. Conclusion This fully automated CT-based liver fat quantification tool allows for population-based assessment of hepatic steatosis and nonalcoholic fatty liver disease, with objective data that match well with manual measurement. The prevalence of at least mild steatosis was greater than 50% in this asymptomatic screening cohort. © RSNA, 2019
Graffy et al. (Tue,) conducted a cohort in Hepatic steatosis (n=9,552). Automated deep learning-based liver fat quantification tool vs. Manual region-of-interest (ROI) measurement was evaluated on Agreement between automated and manual liver attenuation measurements (r2 = 0.92). A fully automated deep learning-based CT liver fat quantification tool demonstrated excellent agreement with manual measurements (mean difference 2.7 HU, r2=0.92) and identified a >50% prevalence of at least mild hepatic steatosis in an asymptomatic screening cohort.