Age- and sex-related changes in proximal humeral volumetric BMD assessed via chest CT with a deep learning-based segmentation model.
Authors
Affiliations (9)
Affiliations (9)
- Orthopedic Medical Center, The Second Hospital of Jilin University, 4026 Yatai Street, Changchun, 130041, China.
- Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of Ministry of Education, Jilin University, Changchun, China.
- College of Computer Science and Technology, Jilin University, Changchun, China.
- Orthopedic and Traumatology, The University of Hong Kong, Hong Kong, China.
- Dipartimento Di Ingegneria E Scienza Dell'Informazione, University of Trento, Trento, Italy.
- Orthopedic Medical Center, The Second Hospital of Jilin University, 4026 Yatai Street, Changchun, 130041, China. [email protected].
- Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of Ministry of Education, Jilin University, Changchun, China. [email protected].
- Orthopedic Medical Center, The Second Hospital of Jilin University, 4026 Yatai Street, Changchun, 130041, China. [email protected].
- Joint International Research Laboratory of Ageing Active Strategy and Bionic Health in Northeast Asia of Ministry of Education, Jilin University, Changchun, China. [email protected].
Abstract
Accurate assessment of proximal humeral volumetric bone mineral density (vBMD) is essential for surgical planning in shoulder pathology. However, age-related changes in proximal humeral vBMD remain poorly characterized. This study developed a deep learning-based method to assess proximal humeral vBMD and identified sex-specific age-related changes. It also demonstrated that lumbar spine vBMD is not a valid substitute. This study aimed to develop a deep learning-based method for proximal humeral vBMD assessment and to investigate its age- and sex-related changes, as well as its correlation with lumbar spine vBMD. An nnU-Net-based deep learning pipeline was developed to automatically segment the proximal humerus on chest CT scans from 2,675 adults. Segmentation performance was assessed using the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), 95th-percentile Hausdorff Distance (95HD), and Average Symmetric Surface Distance (ASSD). Phantom-calibrated vBMD-total, trabecular, and BMAT-corrected trabecular-was quantified for each subject. Age-related distributions were modeled with generalized additive models for location, scale, and shape (GAMLSS) to generate sex-specific P3-P97 percentile curves. Lumbar spine vBMD was measured in 1460 individuals for correlation analysis. Segmentation was highly accurate (DSC 98.42 ± 0.20%; IoU 96.89 ± 0.42%; 95HD 1.12 ± 0.37 mm; ASSD 0.94 ± 0.31 mm). In males, total, trabecular, and BMAT-corrected trabecular vBMD declined approximately linearly from early adulthood. In females, a pronounced inflection occurred at ~ 40-45 years: values were stable or slightly rising beforehand, then all percentiles dropped steeply and synchronously, indicating accelerated menopause-related loss. In females, vBMD declined earlier in the lumbar spine than in the proximal humerus. Correlations between proximal humeral and lumbar spine vBMD were low to moderate overall and weakened after age 50. We present a novel, automated method for quantifying proximal humeral vBMD from chest CT, revealing distinct, sex-specific aging patterns. Males' humeral vBMD declines linearly, while females experience an earlier, accelerated loss. Moreover, the peak humeral vBMD in females occurs later than that of the lumbar spine, and spinal measurements cannot reliably substitute for humeral BMD in clinical assessment.