Artit Jirapatnakul1, Anthony P Reeves2, Sara Lewis1, Xiangmeng Chen3, Teng Ma4, Rowena Yip1, Xing Chin1, Shuang Liu2, Ponni V Perumalswami5, David F Yankelevitz1, Michael Crane6, Andrea D Branch5, Claudia I Henschke7. 1. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, Box 1234, New York, NY 10029, United States. 2. School of Electrical and Computer Engineering, Cornell University, 392 Rhodes Hall, Ithaca, NY 14853, United States. 3. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, Box 1234, New York, NY 10029, United States; Department of Radiology, The First Affiliated Hospital of Jinan University, Huangpu West Ave No. 613, Guangzhou, Guangdong, China; Department of Radiology, Jiangmen Central Hospital, No. 18 Zicha Road, Jiangmen, Guangdong, China. 4. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, Box 1234, New York, NY 10029, United States; Department of Radiology, Jiangmen Central Hospital, No. 18 Zicha Road, Jiangmen, Guangdong, China. 5. Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, Box 1123, New York, NY 10029, United States. 6. Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, 1468 Madison Ave, New York, NY 10029, United States. 7. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, Box 1234, New York, NY 10029, United States. Electronic address: claudia.henshke@mountsinai.org.
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.
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.
Authors: Mary Kay Washington; Jordan Berlin; Philip A Branton; Lawrence J Burgart; David K Carter; Carolyn C Compton; Wendy L Frankel; J Milburn Jessup; Sanjay Kakar; Bruce Minsky; Raouf E Nakhleh; Jean-Nicolas Vauthey Journal: Arch Pathol Lab Med Date: 2010-04 Impact factor: 5.534
Authors: Rowena Yip; Artit Jirapatnakul; Minxia Hu; Xiangmeng Chen; Dan Han; Teng Ma; Yeqing Zhu; Mary M Salvatore; Laurie R Margolies; David F Yankelevitz; Claudia I Henschke Journal: Transl Lung Cancer Res Date: 2021-02