Evidence map›Paper›PMID 41952243›Full record

ArticleAnnals of Indian Academy of Neurology2026

Muscle Magnetic Resonance Imaging Phenotyping and Pattern Recognition in Genetically Confirmed Myopathies: A Large-Cohort Study from the Indian Subcontinent.

Shariq Ahmad Shah, Venugopalan Y Vishnu, Leve Joseph Devaranjan Sebastian, Ajay Garg

Abstract read
In one paragraph

Article in Annals of Indian Academy of Neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Shariq Ahmad ShahDepartment of Neuroimaging and Interventional Neuroradiology, All India Institute of Medical Sciences, New Delhi, India.
Venugopalan Y VishnuDepartment of Neurology, All India Institute of Medical Sciences, New Delhi, India.
Leve Joseph Devaranjan SebastianDepartment of Neuroimaging and Interventional Neuroradiology, All India Institute of Medical Sciences, New Delhi, India.
Ajay GargDepartment of Neuroimaging and Interventional Neuroradiology, All India Institute of Medical Sciences, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesDiagnosing myopathy subtypes is challenging due to clinical and genetic heterogeneity. While muscle magnetic resonance imaging (MRI) enables pattern recognition, standardized imaging data from India are lacking. This study aimed to define MRI patterns in myopathies, compare semi-quantitative scores with fat fraction (FF) analysis, and derive a diagnostic algorithm.

methodsIn this study, a total of 102 patients with confirmed dystrophic or inflammatory myopathies underwent 3T MRI of the pelvic girdle and lower limbs, combining conventional sequences with Dixon-based fat quantification. Analysis used a modified Mercuri T1 scale and Stramare T2 edema scoring, along with FF measurements across 6,180 muscles. Disease patterns, correlations between imaging and clinical variables, and associations between qualitative and quantitative metrics were analyzed.

resultsMRI patterns were distinct for each myopathy. Facioscapulohumeral dystrophy showed hamstring involvement with low asymmetry (5%). Dystrophinopathies exhibited a "trefoil with single fruit" sign (68%). Calpainopathy showed symmetrical end-stage involvement of the gluteal, adductor, and hamstring muscles, while dysferlinopathy affected the gluteus minimus and posterior compartment. Glucosamine-N-acetyl Epimerase (GNE) myopathy showed severe involvement of the gluteus minimus, sartorius, gracilis, and tibialis anterior muscles. In inflammatory myopathies, dermatomyositis showed edema (62%) without a fixed pattern, whereas inclusion body myositis affected the gastrocnemius and gluteal muscles. Disease duration correlated with T1 scores (r = 0.3, P = 0.001) and FF (r = 0.4, P = 0.003). A significant association ( P < 0.001) was observed between T1 scores and FF categories.

conclusionsCombined semi-quantitative scoring and FF MRI distinguished myopathy subtypes, correlated with disease duration, and supported the use of MRI as a biomarker. Our muscle atlas defines disease-specific imaging phenotypes and serves as a reference for diagnostic evaluation in diverse populations.

Indexed as

diagnostic algorithmDixon techniquefat fractionMuscle MRImyopathy

Identifiers

PMID41952243
PMCPMC13193624

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.