Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration

Computational and Mathematical Methods in Medicine, 2016 · DOI: http://dx.doi.org/10.1155/2016/8932950 · Published: December 7, 2016

Medical Imaging Bioinformatics Musculoskeletal Medicine

Simple Explanation

Muscle degeneration is a risk factor for mortality, especially in aging populations or those with neuromuscular issues. A precise method for quantifying this degeneration is still needed. This study introduces a new method using nonlinear trimodal regression analysis on CT scans to assess muscle quality. The method analyzes radiodensitometric distributions of upper leg muscles. It was tested on a healthy young adult, a healthy elderly subject, and a spinal cord injury patient, as well as a cohort of total hip arthroplasty (THA) patients before and after surgery. The study found that this new method can highlight physiological differences between subjects and can show improvements in muscle quality after surgery in THA patients, suggesting its utility in quantifying muscle degeneration.

Study Duration

Not specified

Participants

Healthy young adult, healthy elderly subject, spinal cord injury patient, and 15 THA patients

Evidence Level

Not specified

Key Findings

1
The study found significant qualitative differences in the shapes of HU distributions among healthy, elderly, and pathological subjects. The healthy subject had a high-amplitude muscle peak, while the elderly subject had a more pronounced fat peak.
2
Regression analysis parameters revealed distinct differences between subjects. For example, the elderly subject had a fat amplitude fourfold larger than others, and the control subject's muscle amplitude was at least twofold larger.
3
Analysis of THA cohort data indicated significant improvements in muscle quality in both legs following surgery, supporting the notion that each HU distribution parameter has specificity in muscle assessment.

Research Summary

This study introduces a novel nonlinear trimodal regression analysis methodology for quantifying muscle degeneration using radiodensitometric distributions from CT scans. The method was tested on a range of subjects, including healthy individuals, patients with spinal cord injuries, and THA patients, demonstrating its ability to highlight physiological differences and post-surgical improvements. The findings suggest the potential utility of this method as a straightforward indicator for muscle degeneration and a pivotal step towards developing a new gold standard for muscle analysis.

Practical Implications

Improved Muscle Assessment

The novel method offers a more detailed and potentially more accurate way to assess muscle quality compared to existing methods that rely on average HU values.

Clinical Applications

The method could be used to monitor muscle degeneration in patients with sarcopenia, cachexia, or neuromuscular disorders, and to assess the effectiveness of interventions such as exercise or electrical stimulation.

Personalized Treatment

By providing more detailed information about muscle composition and quality, the method could help to tailor treatments to individual patients' needs.

Study Limitations

1
The study acknowledges that using more subjects will be essential to reinforcing the physiological claims reported.
2
The physiological interpretation of width as a parameter is somewhat obscure.
3
The study acknowledges that the partial volume effect may influence the accuracy of tissue segmentation.

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