Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans. Issue 122 (January 2020)

• Record Type: Journal Article
• Authors: Jirapatnakul, Artit; Reeves, Anthony P.; Lewis, Sara; Chen, Xiangmeng; Ma, Teng; Yip, Rowena; Chin, Xing; Liu, Shuang; Perumalswami, Ponni V.; Yankelevitz, David F.; Crane, Michael; Branch, Andrea D.; Henschke, Claudia I.
• Is Part Of: European journal of radiology. Issue 122(2020)
• Publication Date: 2020-01
• Subjects: LDCT low-dose CT -- HS hepatic steatosis -- ICC intraclass correlation coefficient -- LoA limits of agreement -- NAFLD non-alcoholic fatty liver disease -- NASH non-alcoholic steatohepatitis -- ROI region of interest -- IQR inter-quartile range -- FF fat-fraction -- SD standard deviation -- CI confidence interval -- PPV positive predictive value -- NPV negative predictive value; Hepatic steatosis -- Low-dose CT -- Image analysis -- Lung screening; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2019.108723
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Low-dose chest CT scans can identify moderate-to-severe hepatic steatosis (HS). Automated measurement of liver attenuation agrees with manual ROI measurement. Automated and manual CT identification of moderate-to-severe HS agree with MRI. Abstract: Purpose: Develop and validate an automated method for measuring liver attenuation in non-contrast low-dose chest CT (LDCT) scans and compare it to the standard manual method for identifying moderate-to-severe hepatic steatosis (HS). Method: The automated method identifies a region below the right lung within the liver and uses statistical sampling techniques to exclude non-liver parenchyma. The method was used to assess moderate-to-severe HS on two IRB-approved cohorts: 1) 24 patients with liver disease examined between 1/2013–1/2017 with non-contrast chest CT and abdominal MRI scans obtained within three months of liver biopsy, and 2) 319 lung screening participants with baseline LDCT performed between 8/2011–1/2017. Agreement between the manual and automated CT methods, the manual MRI method, and pathology for determining moderate-to-severe HS was assessed using Cohen's Kappa by applying a 40 HU threshold to the CT method and 17.4% fat fraction to MRI. Agreement between the manual and automated CT methods was assessed using the intraclass correlation coefficient (ICC). Variability was assessed using Bland-Altman limits of agreement (LoA). Results: In the first cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.97, κ = 1.00) with LoA of −7.6 to 4.7 HU. Both manual and automated CT methods had almost perfect agreement with MRI ( κ = 0.90) and substantial agreement with pathology ( κ = 0.77). In the second cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.94, κ = 0.87). LoA were −10.6 to 5.2 HU. Conclusion: Automated measurements of liver attenuation from LDCT scans can be used to identify moderate-to-severe HS on LDCT.