Related Experiment Video
Updated: Mar 1, 2026
08:52
3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
Published on: November 27, 2017
24.4K
Quantitative Muscle Ultrasonography Using Textural Analysis in Amyotrophic Lateral Sclerosis
Jacinto Javier Martínez-Payá1, José Ríos-Díaz2,3, María Elena Del Baño-Aledo4
11 Faculty of Health Sciences, Universidad Católica de Murcia, Murcia, Spain.
Ultrasonic Imaging
|May 30, 2017
Summary
New textural parameters from muscle ultrasound, including gray-level co-occurrence matrix (GLCM) and echovariation (EV), show promise in diagnosing amyotrophic lateral sclerosis (ALS). Combining these with muscle thickness (MTh) significantly improves diagnostic accuracy across multiple muscle groups.
Area of Science:
- Neurology
- Biomedical Engineering
- Medical Imaging
Background:
- Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease affecting motor neurons.
- Early and accurate diagnosis of ALS is crucial for patient management and therapeutic interventions.
- Quantitative muscle ultrasound (MUS) offers a non-invasive method for assessing muscle pathology.
Purpose of the Study:
- To investigate differences in gray-level co-occurrence matrix (GLCM) parameters using MUS between ALS patients and healthy controls.
- To compare the diagnostic performance of GLCM parameters against established first-order MUS parameters (echointensity, echovariation, muscle thickness).
- To evaluate the combined diagnostic accuracy of novel textural MUS parameters with conventional ones in ALS detection.
Main Methods:
- Cross-sectional study involving 26 ALS patients and 26 healthy controls.
- Bilateral and transverse ultrasound imaging of biceps/brachialis, forearm flexor, quadriceps femoris, and tibialis anterior muscles.
- Analysis of GLCM parameters, echointensity (EI), echovariation (EV), and muscle thickness (MTh) using Image J software.
- Statistical analysis including logistic regression and receiver operating characteristic (ROC) curves to determine diagnostic accuracy (sensitivity, specificity, AUC).
Main Results:
- GLCM parameters revealed reduced muscle granularity in ALS patients compared to controls.
- Echovariation (EV) was the best single diagnostic parameter for biceps brachialis and tibialis anterior.
- GLCM was the optimal single parameter for forearm flexors and quadriceps femoris.
- The combination of EV, GLCM, and MTh achieved an area under the curve (AUC) exceeding 90% across all muscle groups.
Conclusions:
- Novel textural parameters derived from MUS, specifically GLCM and EV, demonstrate significant differences between ALS patients and healthy individuals.
- These textural parameters, particularly when combined with muscle thickness, offer high diagnostic accuracy for ALS detection.
- The integration of advanced MUS textural analysis represents a promising non-invasive biomarker strategy for ALS diagnosis.
