Skeletal muscle magnetic resonance imaging in <scp>Pompe</scp> disease

Díaz‐Manera, Jordi; Walter, Glenn A.; Straub, Volker

doi:10.1002/mus.27099

Cited by 21 publications

(22 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering patterns of muscle involvement, although not necessary for differential diagnosis in most cases, its recognition has some similarities with other muscle diseases that is worth mentioning. Tongue and paraspinalis are early and frequently affected in late‐onset Pompe disease (LOPD) [ 36 ] which can also show a similar pattern, but in LOPD quadriceps shows a prominent involvement of vastus intermedius, and gastrocnemius medialis is usually affected later in the disease course when other thigh muscles (semimembranosus and bicep femoris long head) become affected [ 12 ]. As well, tongue, paraspinalis and gluteal muscles are frequently affected in oculopharyngeal muscular dystrophy (OPMD), but with a prominent involvement of the soleus in the leg instead of gastrocnemius medialis [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Garibaldi

Nicoletti

Bucci

et al. 2021

Euro J of Neurology

Self Cite

View full text Add to dashboard Cite

Background Only a few studies have reported muscle imaging data on small cohorts of patients with myotonic dystrophy type 1 (DM1). We aimed to investigate the muscle involvement in a large cohort of patients in order to refine the pattern of muscle involvement, to better understand the pathophysiological mechanisms of muscle weakness, and to identify potential imaging biomarkers for disease activity and severity. Methods One hundred and thirty‐four DM1 patients underwent a cross‐sectional muscle magnetic resonance imaging (MRI) study. Short tau inversion recovery (STIR) and T1 sequences in the lower and upper body were analyzed. Fat replacement, muscle atrophy and STIR positivity were evaluated using three different scales. Correlations between MRI scores, clinical features and genetic background were investigated. Results The most frequent pattern of muscle involvement in T1 consisted of fat replacement of the tongue, sternocleidomastoideus, paraspinalis, gluteus minimus, distal quadriceps and gastrocnemius medialis. Degree of fat replacement at MRI correlated with clinical severity and disease duration, but not with CTG expansion. Fat replacement was also detected in milder/asymptomatic patients. More than 80% of patients had STIR‐positive signals in muscles. Most DM1 patients also showed a variable degree of muscle atrophy regardless of MRI signs of fat replacement. A subset of patients (20%) showed a ‘marbled’ muscle appearance. Conclusions Muscle MRI is a sensitive biomarker of disease severity alsofor the milder spectrum of disease. STIR hyperintensity seems to precede fat replacement in T1. Beyond fat replacement, STIR positivity, muscle atrophy and a ‘marbled’ appearance suggest further mechanisms of muscle wasting and weakness in DM1, representing additional outcome measures and therapeutic targets for forthcoming clinical trials.

show abstract

Section: Discussionmentioning

confidence: 99%

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Garibaldi

Nicoletti

Bucci

et al. 2021

Euro J of Neurology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Over the past few years, numerous papers showed the key role of muscle MRI in neuromuscular diseases as imaging biomarker [11]. In particular, in some inherited muscular pathologies, the pattern of muscle involvement may contribute to address the specific molecular diagnosis [12][13][14][15][16][17]. The characteristic multisystem involvement and, consequently, the presence of non-neurological manifestations in ATTRv, may lead to a significant delay in diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Muscle MRI as a Useful Biomarker in Hereditary Transthyretin Amyloidosis: A Pilot Study

Primiano

Verdolotti

D’Apolito

et al. 2021

Genes

View full text Add to dashboard Cite

Hereditary transthyretin amyloidosis (ATTRv, v for variant) is a severe and heterogeneous multisystem condition with a prevalent peripheral nervous system impairment, due to mutations in the transthyretin gene. Considering the introduction of different disease-modifying therapies in the last few years, a need of reliable biomarkers is emerging. In this study, we evaluated muscle MRI in a cohort of ATTRv patients in order to establish if the severity of muscle involvement correlated with disease severity. Linear regression analysis showed a significant positive correlation between the total fatty infiltration score and NIS, NIS-LL, and Norfolk, and an inverse correlation with Sudoscan registered from feet. In conclusion, we demonstrated the role of muscle MRI in ATTRv as possible disease biomarker, both for diagnostic purposes and for assessing the severity of the disease.

show abstract

“…Quantitative magnetic resonance imaging (qMRI) provides promising surrogate biomarkers in the evaluation of disease progression and monitoring of therapeutic options in neuromuscular diseases (NMD) [1,2]. This non-invasive technique can reveal subclinical changes in muscle pathophysiology which can precede changes in muscle function assessed by clinical examination [3]. In NMD different patterns of muscle involvement have been described and are essential to distinguish between different subtypes of diseases [4,5].…”

Section: Introductionmentioning

confidence: 99%

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

et al. 2021

View full text Add to dashboard Cite

Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions.

show abstract

Skeletal muscle magnetic resonance imaging in Pompe disease

Cited by 21 publications

References 83 publications

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Muscle MRI as a Useful Biomarker in Hereditary Transthyretin Amyloidosis: A Pilot Study

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

Contact Info

Product

Resources

About