Purpose To develop a deep learning model for segmenting pectoralis muscle volume (PMV) at CT and evaluate the reproducibility, group differences, and associations of pectoralis muscle area (PMA) and PMV with chronic obstructive pulmonary disease (COPD)-related outcomes. Materials and Methods This study was a secondary analysis of the prospective Canadian Cohort Obstructive Lung Disease study (CanCOLD, data collected from November 2009 to July 2015). Randomly sampled CT scans from CanCOLD were used for model training, validation, and internal testing (n = 96, 16, and 32, respectively) and an external dataset for external testing (n = 32). A U-Net model was trained for PMV segmentation, and performance was assessed using the Dice similarity coefficient (DSC). PMA and PMV values were extracted from paired inspiration and expiration scans to assess segmentation reproducibility. Differences between individuals with or without COPD and associations with forced expiratory volume in 1 second (FEV1), diffusing capacity of the lungs for carbon monoxide (Dlco), and peak oxygen uptake during exercise (VO2) were reported. Results Individuals included those with (n = 634; mean age, 67.3 years ± 10.1 SD; 394 male participants) and without (n = 601; mean age, 65.8 years ± 9.6; 327 male participants) COPD. The model yielded DSCs of 0.94 ± 0.04, 0.93 ± 0.03, and 0.92 ± 0.04 in the training and validation, internal testing, and external testing datasets, respectively. Contrary to PMV (bias, 0.1 cm3; P = .77), PMA showed bias between inspiration and expiration (bias, -2.7 cm2; P P 1 (adjusted R2 Radj2, 0.609/0.598), Dlco (Radj2, 0.645/0.627), and VO2 (Radj2, 0.680/0.666). Conclusion An accurate and generalizable CT-based deep learning model for pectoralis muscle segmentation was developed. Compared with PMA, PMV showed better reproducibility and stronger associations with COPD outcomes. Keywords: CT, Thorax, Lung, Volume Analysis, Chronic Obstructive Pulmonary Disease, Segmentation ClinicalTrials.gov identifier no. NCT00920348 © RSNA, 2026 Supplemental material is available for this article.
Genkin et al. (Thu,) studied this question.