Diagnostic accuracy of MRI-derived imaging biomarkers and machine learning-based classification in distal and myofibrillar myopathies

OBJECTIVES: To characterise and validate disease-specific muscle imaging patterns in distal and myofibrillar myopathies, quantify their diagnostic performance, and develop an imaging-based classification framework incorporating machine learning. MATERIALS AND METHODS: Muscle MRI and CT scans from a multicentre national cohort of patients with genetically defined distal and myofibrillar myopathies were retrospectively analysed. The pattern of muscle involvement for each disease entity was described, and its sensitivity and specificity calculated. Supervised machine learning algorithms were trained using muscle scores and imaging-derived features, and their diagnostic performance was compared using balanced accuracy. RESULTS: A total of 165 patients (mean age 48 ± 16 years; 87 men), including 138 molecularly confirmed cases spanning 19 disease genes, were analysed. Overall, 6350 muscles were systematically evaluated. Key findings included early biceps femoris short head involvement in GNE myopathy, "fat pockets" in myotilinopathy, and early flexor hallucis longus involvement in desminopathy. All imaging patterns showed high sensitivity and specificity (from 75 to 100%). Among the tested machine learning models, XGBoost achieved the highest performance, correctly distinguishing among 11 genetic disease entities with a balanced accuracy of 69%. CONCLUSION: Distal and myofibrillar myopathies demonstrate reproducible disease-specific imaging signatures with high diagnostic accuracy. Integration of these imaging biomarkers into a machine learning framework enables systematic prioritisation of likely genetic diagnoses and may facilitate diagnostic work-up in these heterogeneous inherited muscle disorders. KEY POINTS: Question How accurately do muscle MRI patterns distinguish distal and myofibrillar myopathies, and how do imaging features perform in an updated MRI-based diagnostic algorithm? Findings Disease-specific muscle MRI patterns showed high diagnostic accuracy, while XGBoost achieved the highest balanced accuracy among machine learning models for imaging-based disease classification. Clinical relevance Refined muscle MRI patterns provide diagnostically useful imaging biomarkers in distal and myofibrillar myopathies. Their integration into machine learning-based classification models may facilitate precision diagnostics and guide targeted genetic testing.

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Publication Details

Journal
European Radiology
Published
2026-10-30
DOI
https://doi.org/10.1007/s00330-026-13014-4
Primary Topic
Muscle Physiology and Disorders
Type
article
Field-Weighted Citation Impact
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article

Diagnostic accuracy of MRI-derived imaging biomarkers and machine learning-based classification in distal and myofibrillar myopathies

Mauro Monforte, Elena Pegoraro, Simonetta Gerevini, Stefano Carlo Previtali et al.
European Radiology
Muscle Physiology and Disorders
article

Diagnostic accuracy of MRI-derived imaging biomarkers and machine learning-based classification in distal and myofibrillar myopathies

Mauro Monforte, Elena Pegoraro, Simonetta Gerevini, Stefano Carlo Previtali, Matteo Garibaldi, Nicola De Stefano, Silvia Bonanno, Massimiliano Mirabella A, Tiziana Mongini, Serenella Servidei, Sara Bortolani, Marco Elvio Manlio Moscatelli, Pietro Riguzzi, Giulio Gadaleta, Amalia Lupi, Eleonora Torchia, Sabrina Ravaglia, Antonio Petrucci, Guido Primiano, Gaetano Nicola Alfio Vattemi, Paola Tonin, Matteo Lucchini, Maria Lucia Valentino, Carmelo Rodolico, Giorgio A. Tasca, Rocco Liguori, Yuri Falzone, Jordi Díaz‐Manera, Lorenzo Maggi, Diego Lopergolo, Marco Savarese, José Verdú-Díaz, Enzo Ricci, Francesca Torri, Gabriele Siciliano, Beatrice Ravera
article en

Abstract

OBJECTIVES: To characterise and validate disease-specific muscle imaging patterns in distal and myofibrillar myopathies, quantify their diagnostic performance, and develop an imaging-based classification framework incorporating machine learning. MATERIALS AND METHODS: Muscle MRI and CT scans from a multicentre national cohort of patients with genetically defined distal and myofibrillar myopathies were retrospectively analysed. The pattern of muscle involvement for each disease entity was described, and its sensitivity and specificity calculated. Supervised machine learning algorithms were trained using muscle scores and imaging-derived features, and their diagnostic performance was compared using balanced accuracy. RESULTS: A total of 165 patients (mean age 48 ± 16 years; 87 men), including 138 molecularly confirmed cases spanning 19 disease genes, were analysed. Overall, 6350 muscles were systematically evaluated. Key findings included early biceps femoris short head involvement in GNE myopathy, "fat pockets" in myotilinopathy, and early flexor hallucis longus involvement in desminopathy. All imaging patterns showed high sensitivity and specificity (from 75 to 100%). Among the tested machine learning models, XGBoost achieved the highest performance, correctly distinguishing among 11 genetic disease entities with a balanced accuracy of 69%. CONCLUSION: Distal and myofibrillar myopathies demonstrate reproducible disease-specific imaging signatures with high diagnostic accuracy. Integration of these imaging biomarkers into a machine learning framework enables systematic prioritisation of likely genetic diagnoses and may facilitate diagnostic work-up in these heterogeneous inherited muscle disorders. KEY POINTS: Question How accurately do muscle MRI patterns distinguish distal and myofibrillar myopathies, and how do imaging features perform in an updated MRI-based diagnostic algorithm? Findings Disease-specific muscle MRI patterns showed high diagnostic accuracy, while XGBoost achieved the highest balanced accuracy among machine learning models for imaging-based disease classification. Clinical relevance Refined muscle MRI patterns provide diagnostically useful imaging biomarkers in distal and myofibrillar myopathies. Their integration into machine learning-based classification models may facilitate precision diagnostics and guide targeted genetic testing.

European Radiology
University of Siena (IT), Università Cattolica del Sacro Cuore (IT), University of Pisa (IT), University of Messina (IT), University of Verona (IT), University of Padua (IT), Vita-Salute San Raffaele University (IT), Newcastle upon Tyne Hospitals NHS Foundation Trust (GB), Hospital de Sant Pau (ES), Agostino Gemelli University Polyclinic (IT), Azienda Ospedaliera San Camillo-Forlanini (IT), Ospedale Papa Giovanni XXIII (IT), Centre for Biomedical Network Research on Rare Diseases (ES), Centro de Investigación Biomédica en Red (ES), Azienda Ospedaliera Sant'Andrea (IT), IRCCS Ospedale San Raffaele (IT), NIHR Newcastle Biomedical Research Centre (GB), Carlo Forlanini Hospital (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Fondazione IRCCS Istituto Neurologico Carlo Besta (IT), Folkhälsans Forskningscentrum (FI), Institut de Recerca Sant Pau (ES), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), University of Turin (IT), Newcastle University (GB), Sapienza University of Rome (IT), University of Bologna (IT)
Muscular Dystrophy Association, National Institute for Health and Care Research, Newcastle University, Newcastle upon Tyne Hospitals NHS Foundation Trust, AFM-Téléthon, Faculty of Medical Sciences, Newcastle University, NIHR Newcastle Biomedical Research Centre
Good health and well-being
Openalex Percentile: Top 22%
Muscle Physiology and Disorders
DOIOpen Access PDFOpenAlex
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