Lower-limb muscle mass quantification and whole-body muscle loss detection using preoperative computed tomography images in patients with hip disease.

PURPOSE: This study developed a method for assessing lower-limb lean mass measured by dual-energy X-ray absorptiometry (DXA-LMleg), using lower-limb muscle mass derived from computed tomography images (CT-MM). Further, the diagnostic performance of the model in detecting whole-body muscle mass (MM) loss, a key component in the assessment of sarcopenia, was evaluated using CT-MM to facilitate the timely initiation of treatment as needed.

METHODS

This retrospective study enrolled 227 patients who underwent hip surgery at two institutions. A deep neural network (DNN)-based method was employed in segmenting lower-limb CT images taken for surgical planning, and the CT-MM was calculated using two different density conversion methods: CT-MM1 (CT-MM calculated using the conventional method) and CT-MM2 (CT-MM calculated using the method by Aubrey et al.). Both CT-MMs were correlated with DXA-LMleg, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of CT-MMs in detecting whole-body MM loss.

RESULTS

In 222 cases that were successfully automatically analyzed, strong correlations were observed between CT-MM1 and DXA-LMleg (rs = 0.92-0.96) and between CT-MM2 and DXA-LMleg (rs = 0.86-0.92). ROC curve analysis revealed high diagnostic accuracy for whole-body MM loss (CT-MM1, area under the curve (AUC) = 0.96-0.97; CT-MM2, AUC = 0.91-0.93), with CT-MM1 demonstrating significantly better performance.

CONCLUSION

CT-MMs were strongly correlated with DXA-LMleg and had a high diagnostic performance (AUC > 0.9) in detecting whole-body MM loss, supporting sarcopenia screening and preoperative clinical decision-making using routine CT scan.