Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD

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

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 5,535 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),