Evidence map›Paper›PMID 41548716›Full record

ReviewJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2026

Role of cardiovascular magnetic resonance in diagnosis and management of muscular dystrophies.

Vincenzo Russo, Julien Hudelo, Michał Marchel, Jeremy Florence, Gilles Soulat, Robert Manka, Francois Pontana, Jean Nicolas Dacher, Solenn Toupin, Saman Nazarian and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Vincenzo RussoCardiology Unit, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy. Electronic address: vincenzo.russo@unicampania.it.
Julien HudeloMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; Department of Cardiology, Amiens University Hospital, Amiens, France; DATA-TEMPLE laboratory, Department of Data Science, Machine Learning and Artificial Intelligence in Health, University Hospital of Lariboisiere (AP-HP), Paris, France.
Michał Marchel1st Department of Cardiology, Medical University of Warsaw.
Jeremy FlorenceMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; Université Paris Cité, Paris, France; Department of Cardiology, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France; Department of Radiology, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.
Gilles SoulatMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; Université Paris Cité, Department of Radiology, Hôpital European Georges Pompidou, (Assistance Publique des Hôpitaux de Paris, AP-HP), Paris, France.
Robert MankaDepartment of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Francois PontanaMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; Department of Cardiovascular Radiology, Univ. Lille, CHU Lille, Institut Pasteur de Lille, Lille, France.
Jean Nicolas DacherMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; Université de Rouen, Department of Radiology, CHU de Rouen, Rouen, France.
Solenn ToupinMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; DATA-TEMPLE laboratory, Department of Data Science, Machine Learning and Artificial Intelligence in Health, University Hospital of Lariboisiere (AP-HP), Paris, France; Department of Cardiology, University Hospital of Lariboisiere, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.
Saman NazarianDivision of Cardiology, Johns Hopkins Hospital, Baltimore, Maryland, USA.
Gerardo NigroCardiology Unit, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy.
Karim WahbiAPHP, Cochin Hospital, Cardiology Department, Centre de Référence des maladies neuromusculaires Nord/Est/Ile-de-France, Paris, France; Associated laboratory Paris Cardiomyopathy Center, IMAGINE Institute, Paris Cité University, Paris, France.
Theo PezelMIRACL.ai laboratory, Multimodality Imaging for Research and Artificial Intelligence Core Laboratory, University Hospital of Lariboisiere (AP-HP), Paris, France; DATA-TEMPLE laboratory, Department of Data Science, Machine Learning and Artificial Intelligence in Health, University Hospital of Lariboisiere (AP-HP), Paris, France; Associated laboratory Paris Cardiomyopathy Center, IMAGINE Institute, Paris Cité University, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Muscular dystrophies encompass a heterogeneous spectrum of inherited myopathies characterized by progressive skeletal muscle degeneration frequently accompanied by life-threatening cardiac involvement. Cardiovascular magnetic resonance (CMR) has become the reference non-invasive imaging modality for the detection, characterization, and longitudinal monitoring of cardiomyopathy involvement across this group of disorders. This state-of-the-art review summarized contemporary evidence on the diagnostic and prognostic value of CMR in the most prevalent muscular dystrophies, including Myotonic dystrophy, Duchenne and Becker muscular dystrophies, Emery-Dreifuss muscular dystrophy, laminopathies, facioscapulohumeral muscular dystrophy, and mitochondrial myopathies. CMR uniquely enables high-resolution assessment of ventricular volumes and function, tissue characterization through late gadolinium enhancement (LGE) and parametric mapping (native T1, T2, extracellular volume fraction), and quantitative strain imaging. These techniques uncover subclinical myocardial involvement years before overt dysfunction occurs, providing a robust substrate for early therapeutic intervention. Disease-specific CMR signatures, such as inferolateral subepicardial fibrosis in dystrophinopathies or mid-wall septal enhancement in laminopathies, allow for refined etiological diagnosis and targeted risk stratification. LGE burden and distribution are independently associated with ventricular arrhythmias and adverse cardiac events, transcending the limitations of traditional criteria based on left ventricular ejection fraction for implantable cardioverter-defibrillator selection. Emerging evidence further supports the integration of CMR biomarkers into genotype-guided management strategies and prospective therapeutic trials.

Indexed as

ArrhythmiasCardiovascular magnetic resonanceLate gadolinium enhancementMuscular dystrophyMyotonic dystrophySudden death

Identifiers

PMID41548716
PMCPMC13315846

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Registered trials

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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.