Artificial Intelligence-Derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis

Details

• Journal Article:
  Radiology: Cardiothoracic Imaging
• Personal Author:
  Azimi, Amir ; Atlas, Kyle ; Reeves, Anthony P. ; Zhang, Chenyu ; Wasserthal, Jakob ; Mirjalili, Seyed R. ; Atlas, Thomas ; Henschke, Claudia I. ; Yankelevitz, David F. ; Zulueta, Javier J. ; de-Torres, Juan P. ; Seijo, Luis M. ; Mechanick, Jeffrey I. ; Branch, Andrea D. ; Ma, Ning ; Yip, Rowena ; Fan, Wenjun ; Roy, Sion K. ; Nasir, Khurram ; Molloi, Sabee ; Fayad, Zahi A. ; McConnell, Michael V. ; Kakadiaris, Ioannis A. ; Abela, George S. ; Vliegenthart, Rozemarijn ; Maron, David J. ; Narula, Jagat ; Williams, Kim A. Sr. ; Shah, Prediman K. ; Budoff, Matthew J. ; Levy, Daniel ; Benjamin, Emelia J. ; Mehran, Roxana ; Kloner, Robert A. ; Wong, Nathan D. ; Naghavi, Morteza
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• Description:
  Purpose: To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnosed chronic obstructive pulmonary disease (COPD) and compare it with an artificial intelligence (AI)-measured biomarker of emphysema derived from the same scans. Materials and Methods: In this prospective study, baseline CAC CT scans and 20-year follow-up data were analyzed. Myosteatosis was defined as the lowest quartile of thoracic skeletal muscle mean attenuation (males < 33.5 HU, females < 27.0 HU). The emphysema-like lung biomarker was quantified as the percentage of lung voxels below -950 HU in CAC CT scans. COPD was identified using the International Classification of Diseases, Ninth Revision, Clinical Modification, and International Classification of Diseases, 10th Revision, Clinical Modification diagnostic codes from hospital discharge records. Hazard ratios (HRs) for COPD were calculated using proportional hazard regression models, comparing the bottom versus top quartiles of myosteatosis and emphysema-like lung measurements. Results: Among 5535 participants in the Multi-Ethnic Study of Atherosclerosis (mean age +/- SD, 62.2 years +/- 10.3, 47.6% males), 396 (7.1%) were diagnosed with COPD over the 20-year follow-up period. Myosteatosis showed a stronger association with COPD than emphysema (unadjusted HRs, 5.98 95% CI: 4.14, 8.63 and 2.12 95% CI: 1.61, 2.78, respectively P < .001). After adjusting for covariates (age, sex, smoking status, body mass index, race, asthma, physical activity, inflammatory markers, and insulin resistance), the HRs were reduced to 2.74 (95% CI: 1.81, 4.16) and 1.50 (95% CI: 1.12, 2.00), respectively (P = .02). Conclusion: AI-measured myosteatosis in CAC CT scans strongly predicted future diagnosed COPD independently of known risk factors. Description provided by NIOSH
• Subjects:
  • Artificial Intelligence
  • Atherosclerosis
  • Cardiovascular System
  • Metabolism
  • Occupational Health
  • Pulmonary Disease, Chronic Obstructive
  • Safety
• Keywords:
  • COPD; Chronic obstructive pulmonary disease; Imaging techniques; Artificial intelligence; Biomarkers; Radiology; Metabolic disorders; Muscle tissue; Cardiology; Thorax; Cardiovascular disease;
  • Author Keywords: Applications-CT; Pulmonary; Thorax; Adipose Tissue; Obesity Studies; Chronic Obstructive Pulmonary Disease; Metabolic Disorders; Myosteatosis; Coronary Artery Calcium Scan; Emphysema; AI-CVD
• Source:
  Radiol Cardiothorac Imaging 2026 Feb; 8(1):e250205
• ISSN:
  2638-6135
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Birmingham Region OSHA ; Boston Region OSHA ; California ; Chicago Region OSHA ; Dallas Region OSHA ; Kentucky ; Massachusetts ; Michigan ; New York ; New York City Region OSHA ; San Francisco Region OSHA ; Texas
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• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  10 pdf pages
• Volume:
  8
• Issue:
  1
• NIOSHTIC Number:
  nn:20071629
• Contact Point Address:
  Morteza Naghavi, MD, HeartLung.AI, 2450 Holcombe Blvd, Houston, TX 77021
• Email:
  mn@vp.org
• Federal Fiscal Year:
  2026
• Performing Organization:
  Icahn School of Medicine at Mount Sinai, New York
• Peer Reviewed:
  True
• Start Date:
  20230701
• End Date:
  20260630
• Download URL:
  https://stacks.cdc.gov/view/cdc/259404/cdc_259404_DS1.pdf
• File Type:
  [PDF - 1.12 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:dc8f997df8ccddba85fafcc2a4c94a1034863ae41a5e8bb97a8504e688ed4ad5e2fba9f165e6e4d51bac0cf144bf4f54b32e4b55164450006b952ad1ff31fc13