Journal of neurology · 2026
Imaging-derived neuromuscular ultrasound phenotypes are associated with functional status in amyotrophic lateral sclerosis.
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Wang Ying, Zhang Hao, Yang Tianhua, Luo Jialei, Lin Ting, Yan Xinyi, Ding Junlin, Qiu Yuxuan, Zhao Min, Yang Gaoyi
Аннотация
BACKGROUND: Amyotrophic lateral sclerosis (ALS) presents with marked clinical heterogeneity, complicating diagnosis and management. Neuromuscular ultrasound (NMUS) provides a non-invasive means to visualize peripheral nerve and muscle integrity, but its potential to delineate ALS subtypes has not been systematically explored.
OBJECTIVE: To identify clinically meaningful ALS subgroups through unsupervised clustering of NMUS features integrated with clinical and electrophysiological data.
METHODS: A total of 454 ALS patients (August 2024-December 2025) underwent standardized NMUS assessment, including muscle thickness, echogenicity, and nerve cross-sectional area, alongside ALSFRS-R, manual muscle testing (MMT), and compound muscle action potentials (CMAPs). K-means clustering was applied to standardized NMUS variables, with cluster stability assessed using silhouette coefficients, sensitivity analyses (k = 2-5), and resampling-based adjusted Rand indices. Multivariable regression examined associations between cluster membership and ALSFRS-R.
RESULTS: Two reproducible NMUS-based subgroups were identified: a Mild cluster (n = 288, 63.4%) and a Severe cluster (n = 166, 36.6%). The Severe cluster showed reduced muscle thickness and higher echogenicity across multiple sites, together with lower ALSFRS-R scores (adjusted β = - 3.84, 95% CI - 5.41 to - 2.27, P < 0.001). Cluster membership correlated negatively with MMT and CMAP amplitudes, supporting functional and electrophysiologic validity. Stability metrics confirmed robustness of the two-cluster solution.
CONCLUSION: Integrating NMUS with clinical data enables objective, imaging-derived stratification of ALS patients into biologically and functionally distinct subgroups. This approach offers a pragmatic framework for phenotypic characterization and may inform personalized monitoring and trial design in ALS.
OBJECTIVE: To identify clinically meaningful ALS subgroups through unsupervised clustering of NMUS features integrated with clinical and electrophysiological data.
METHODS: A total of 454 ALS patients (August 2024-December 2025) underwent standardized NMUS assessment, including muscle thickness, echogenicity, and nerve cross-sectional area, alongside ALSFRS-R, manual muscle testing (MMT), and compound muscle action potentials (CMAPs). K-means clustering was applied to standardized NMUS variables, with cluster stability assessed using silhouette coefficients, sensitivity analyses (k = 2-5), and resampling-based adjusted Rand indices. Multivariable regression examined associations between cluster membership and ALSFRS-R.
RESULTS: Two reproducible NMUS-based subgroups were identified: a Mild cluster (n = 288, 63.4%) and a Severe cluster (n = 166, 36.6%). The Severe cluster showed reduced muscle thickness and higher echogenicity across multiple sites, together with lower ALSFRS-R scores (adjusted β = - 3.84, 95% CI - 5.41 to - 2.27, P < 0.001). Cluster membership correlated negatively with MMT and CMAP amplitudes, supporting functional and electrophysiologic validity. Stability metrics confirmed robustness of the two-cluster solution.
CONCLUSION: Integrating NMUS with clinical data enables objective, imaging-derived stratification of ALS patients into biologically and functionally distinct subgroups. This approach offers a pragmatic framework for phenotypic characterization and may inform personalized monitoring and trial design in ALS.