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PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.MethodsTwenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.ResultsThe combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2.ConclusionThis pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.

Imaging has become a valuable tool in the assessment of neuromuscular diseases, and, specifically, quantitative MR imaging provides robust biomarkers for the monitoring of disease progression. Quantitative evaluation of fat infiltration and quantification of the T2 values of the muscular tissue's water component (wT2) are two of the most essential indicators currently used. As each voxel of the image can contain both water and fat, a two-component model for the estimation of wT2 must be used. In this work, we present a fast method for reconstructing wT2 maps obtained from conventional multi-echo spin-echo (MESE) acquisitions and released as Free Open Source Software. The proposed software is capable of fast reconstruction thanks to extended phase graphs (EPG) simulations and dictionary matching implemented on a general-purpose graphic processing unit. The program can also perform more conventional biexponential least-squares fitting of the data and incorporate information from an external water-fat acquisition to increase the accuracy of the results. The method was applied to the scans of four healthy volunteers and five subjects suffering from facioscapulohumeral muscular dystrophy (FSHD). Conventional multi-slice MESE acquisitions were performed with 17 echoes, and additionally, a 6-echo multi-echo gradient-echo (MEGE) sequence was used for an independent fat fraction calculation. The proposed reconstruction software was applied on the full datasets, and additionally to reduced number of echoes, respectively, to 8, 5, and 3, using EPG and biexponential least-squares fitting, with and without incorporating information from the MEGE acquisition. The incorporation of external fat fraction maps increased the robustness of the fitting with a reduced number of echoes per datasets, whereas with unconstrained fitting, the total of 17 echoes was necessary to retain an independence of wT2 from the level of fat infiltration. In conclusion, the proposed software can successfully be used to calculate wT2 maps from conventional MESE acquisition, allowing the usage of an optimized protocol with similar precision and accuracy as a 17-echo acquisition. As it is freely released to the community, it can be used as a reference for more extensive cohort studies.

Objective: Neurological sequelae of SARS-CoV-2 infection have already been reported, but there is insufficient data about the impact of the pandemic on the management of the patients with chronic neurological diseases. We aim to analyze the effect of COVID-19 pandemic and social restriction rules on these fragile patients.Methods: Patients with chronic neurologic diseases routinely followed at the outpatient clinic of Gemelli University Hospital, Rome, were assessed for symptoms suggestive of SARS-CoV-2 infection in the pandemic period, consequences of social restrictions, and neurological disease features, concomitant medical conditions, current medical and disease-specific treatments. Data source: a dedicated telephone survey designed to encompass questions on COVID-19 symptoms and on pandemic effects in chronic neurologic conditions.Results: Overall, 2,167 individuals were analyzed: 63 patients reported contact with COVID-19 positive cases, 41 performed the swab, and 2 symptomatic patients tested positive for COVID-19 (0.09%). One hundred fifty-eight individuals (7%) needed urgent neurological care, deferred due to the pandemic; 641 patients (30%) suspended hospital treatments, physiotherapy or other support interventions; 405 individuals (19%) reported a subjective worsening of neurological symptoms.Conclusions: In our population, the presence of neurological chronic diseases did not increase the prevalence of COVID-19 infection. Nevertheless, the burden of neurological disorders has been worsened by the lockdown.

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder which is typically transmitted by an autosomal dominant pattern, although reduced penetrance and sporadic cases caused by de novo mutations, are often observed. FSHD may be caused by a contraction of a repetitive element, located on chromosome 4 (4q35). This locus is named D4Z4 and consists of 11 to more than 100 repeated units (RU). The D4Z4 is normally hypermethylated and the genes located on this locus are silenced. In case of FSHD, the D4Z4 region is characterized by 1–10 repeats and results in the region being hypomethylated. However, 5% of FSHD cases do not carry the short allele of D4Z4 region. To date, two forms of FSHD (FSHD1 and FSHD2) are known. FSHD2 is usually observed in patients without the D4Z4 fragment contraction and carrying variants in SMCHD1 (18p11.32) gene. We report the case of a young adult patient who shows severe symptoms of FSHD. Preliminary genetic analysis did not clarify the phenotype, therefore we decided to study the family members by genetic and epigenetic approaches. The analysis of D4Z4 fragment resulted to be 8 RU in the affected proband and in his father; 26 RU in the mother and 25 RU in the maternal uncle. SMCHD1 analysis revealed a heterozygous variation within the exon 41. The variant was detected in the proband, her mother and the uncle. Furthermore, epigenetic analysis of CpG6 methylation regions showed significant hypomethylation in the affected patient (54%) and in the mother (56%), in contrast to the father (88%) and the uncle (81%) carrying higher methylation levels. The analysis of DR1 methylation levels reported hypomethylation for the proband (19%), the mother (11%), and the uncle (16%). The father showed normal DR1 methylation levels (>30%). Given these results, the combined inheritance of SMCHD1 variant and the short fragment might explain the severe FSHD phenotype displayed by the proband. On this subject, SMCHD1 analysis should be promoted in a larger number of patients, even in presence of D4Z4 contractions, to facilitate the genotype-phenotype correlation as well as, to enable a more precise diagnosis and prognosis of the disease.