ReviewNeurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics2020
The Value of Imaging and Composition-Based Biomarkers in Duchenne Muscular Dystrophy Clinical Trials.
Review in Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review.Muscle & nerve · 2021Pooled it
- Relationship Between Quantitative Magnetic Resonance Imaging Measures and Functional Changes in Patients With Duchenne Muscular Dystrophy.Muscle & nerve · 2025Trial
- Quantitative Muscle Ultrasound: A Non-Invasive Biomarker for Monitoring Duchenne Muscular Dystrophy.Muscle & nerve · 2025Observational
- The Diagnostic Value of Musculoskeletal Ultrasound in the Quantitative Evaluation of Skeletal Muscle in Chronic Thyrotoxic Myopathy: A Single-Center Study in China.International journal of general medicine · 2024Article
- Review
- Utilization of T1-Mapping for the pelvic and thigh muscles in Duchenne Muscular Dystrophy: a quantitative biomarker for disease involvement and correlation with clinical assessments.BMC musculoskeletal disorders · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the drug development pipeline for Duchenne muscular dystrophy (DMD) rapidly advances, clinical trial outcomes need to be optimized. Effective assessment of disease burden, natural history progression, and response to therapy in clinical trials for Duchenne muscular dystrophy are critical factors for clinical trial success. By choosing optimal biomarkers to better assess therapeutic efficacy, study costs and sample size requirements can be reduced. Currently, functional measures continue to serve as the primary outcome for the majority of DMD clinical trials. Quantitative measures of muscle health, including magnetic resonance imaging and spectroscopy, electrical impedance myography, and ultrasound, sensitively identify diseased muscle, disease progression, and response to a therapeutic intervention. Furthermore, such non-invasive techniques have the potential to identify disease pathology prior to onset of clinical symptoms. Despite robust supportive evidence, non-invasive quantitative techniques are still not frequently utilized in clinical trials for Duchenne muscular dystrophy. Non-invasive quantitative techniques have demonstrated the ability to quantify disease progression and potential response to therapeutic intervention, and should be used as a supplement to current standard functional measures. Such methods have the potential to significantly accelerate the development and approval of therapies for DMD.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.