3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults.

Background: In order to identify patients with sarcopenia, the use of routine imaging could provide valuable support. One of the most common radiological examinations, especially in geriatric inpatient care, is CT thoracic imaging. Therefore, it would be desirable to generate muscle volumes from these images using automated body composition analysis. The aim of this study is to determine the muscle volumes of geriatric patients and to investigate to what extent these correspond to the values of one of the current reference standards in diagnosing sarcopenia, the Dual-energy X-ray Absorptiometry (DXA) measurement.

Methods: This retrospective study included 208 geriatric patients (mean age: 81 ± 7 years, 53.4% women) treated at the Acute Geriatric Ward at LMU University Hospital between 2015 and 2022. All participants underwent DXA measurement to assess appendicular skeletal muscle mass (ASM). Pretrained deep learning models were used to analyze body composition from routinely obtained thoracic CT images. Correlations between CT and DXA data were calculated using Pearson correlations, taking into account different normalization variants (height², weight, bone volume and total volume). Multivariable linear regression analysis was performed to predict DXA-measured ASM.

Results: Women and men differed significantly in bone volume, muscle volume, and intramuscular fat. A reliable correlation was found between muscle volume from CT-thorax analysis and ASM from DXA, especially for absolute muscle volume (r = 0.669, p < 0.001) and muscle volume normalized to height² (r = 0.529, p < 0.001). In regression analysis, CT muscle volume alone explained 44.5% of the variance in ASM (R² = 0.445, p < 0.001). When body weight was added, the model's explanatory power increased significantly to 68.9% (R² = 0.689, p < 0.001). The fully adjusted model, which included height, age, and sex, further improved the explained variance only slightly (R² = 0.713, p < 0.001). Among all predictors, body weight showed the strongest effect, followed by CT muscle volume, while sex had no significant influence.

Conclusion: The results show that the automated analysis of CT thoracic scans is a useful method for determining muscle volume and agrees well with the DXA analysis. Furthermore, the predictive value of CT muscle volume is significantly enhanced in combination with anthropometric parameters, particularly body weight. Further prospective studies are required to validate the findings and refine CT-based sarcopenia diagnostics.