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Magnetic resonance imaging patterns of muscle involvement in genetic muscle diseases: a systematic review.

ABSTRACT: A growing body of the literature supports the use of magnetic resonance imaging as a potential biomarker for disease severity in the hereditary myopathies. We performed a systematic review of the medical literature to evaluate patterns of fat infiltration observed in magnetic resonance imaging studies of muscular dystrophy and congenital myopathy. Searches were performed using MEDLINE, EMBASE, and grey literature databases. Studies that described fat infiltration of muscles in patients with muscular dystrophy or congenital myopathy were selected for full-length review. Data on preferentially involved or spared muscles were extracted for analysis. A total of 2172 titles and abstracts were screened, and 70 publications met our criteria for inclusion in the systematic review. There were 23 distinct genetic disorders represented in this analysis. In most studies, preferential involvement and sparing of specific muscles were reported. We conclude that magnetic resonance imaging studies can be used to identify distinct patterns of muscle involvement in the hereditary myopathies. However, larger studies and standardized methods of reporting are needed to develop imaging as a diagnostic tool in these diseases.

SUBMITTER: Leung DG

PROVIDER: S-EPMC5445016 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Magnetic resonance imaging patterns of muscle involvement in genetic muscle diseases: a systematic review.

Leung Doris G DG

Journal of neurology 20161125 7

A growing body of the literature supports the use of magnetic resonance imaging as a potential biomarker for disease severity in the hereditary myopathies. We performed a systematic review of the medical literature to evaluate patterns of fat infiltration observed in magnetic resonance imaging studies of muscular dystrophy and congenital myopathy. Searches were performed using MEDLINE, EMBASE, and grey literature databases. Studies that described fat infiltration of muscles in patients with musc ...[more]

PMID: 27888415

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Project description:(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

Project description:BackgroundDiagnosis of mandibular involvement caused by head and neck cancers is critical for treatment. We performed a meta-analysis to determine the diagnostic efficacy of MR for distinguishing mandibular involvement caused by head and neck cancers.MethodsThirteen databases were searched electronically and hand-searching was also done. Two reviewers conducted study inclusion, data extractions, and quality assessment of the studies independently. Meta-disc 1.4 and STATA 11.0 were used to conduct the meta-analysis.Results16 studies involving a total of 490 participants underwent MR examinations and were accounted for in this meta-analysis. Among the included studies, 2 had high risk of bias, while the rest had unclear risk of bias. Meta-regression showed that the slight clinical and methodological heterogeneities did not influence the outcome (P>0.05). Meta-analysis indicated that the MR for the diagnosis of mandibular involvement had a pooled sensitivity (SEN) of 78%, specificity (SPE) of 83%, positive likelihood ratio (+LR) of 3.80, negative likelihood ratio (-LR) of 0.28, diagnostic odds ratio (DOR) of 28.94, area under curve (AUC) of 0.9110, and Q* of 0.8432. Two studies detected the diagnostic efficacy of MR for the mandibular medullar invasion, and only one study reported the inferior alveolar canal invasion, which made it impossible to include it in our meta-analysis. In comparing to CT, MR had a higher SEN without statistical significance (P = 0.08), but a significantly lower SPE (P = 0.04). The synthesized diagnostic efficacy (AUC and Q*) on mandibular involvement was similar between the two modalities (P>0.05).ConclusionsPresent clinical evidence showed that MR had an acceptable diagnostic value in detecting mandibular involvement caused by head and neck cancers. MR exceeded CT in diagnosing patients with mandibular invasion (higher sensitivity than CT) but was less efficacious to exclude patients without the mandibular invasion (lower specificity than CT).

Project description:ContextMultiparametric magnetic resonance imaging (mpMRI) detects most, but not all, clinically significant prostate cancer. The genetic basis of prostate cancer visibility and invisibility on mpMRI remains uncertain.ObjectiveTo systematically review the literature on differential gene expression between mpMRI-visible and mpMRI-invisible prostate cancer, and to use bioinformatic analysis to identify enriched processes or cellular components in genes validated in more than one study.Evidence acquisitionWe performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to January 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The primary endpoint was differential genetic features between mpMRI-visible and mpMRI-invisible tumours. Secondary endpoints were explanatory links between gene function and mpMRI conspicuity, and the prognostic value of differential gene enrichment.Evidence synthesisWe retrieved 445 articles, of which 32 met the criteria for inclusion. Thematic synthesis from the included studies showed that mpMRI-visible cancer tended towards enrichment of molecular features associated with increased disease aggressivity, including phosphatase and tensin homologue (PTEN) loss and higher genomic classifier scores, such as Oncotype and Decipher. Three of the included studies had accompanying publicly available data suitable for further bioinformatic analysis. An over-representation analysis of these datasets revealed increased expression of genes associated with extracellular matrix components in mpMRI-visible tumours.ConclusionsProstate cancer that is visible on mpMRI is generally enriched with molecular features of tumour development and aggressivity, including activation of proliferative signalling, DNA damage, and inflammatory processes. Additionally, there appears to be concordant cellular components and biological processes associated with mpMRI conspicuity, as highlighted by bioinformatic analysis of large genetic datasets.Patient summaryProstate cancer that is detected by magnetic resonance imaging (MRI) tends to have genetic features that are associated with more aggressive disease. This suggests that MRI can be used to assess the likelihood of aggressive prostate cancer, based on tumour visibility.

Dataset Information

Magnetic resonance imaging patterns of muscle involvement in genetic muscle diseases: a systematic review.

Publications

Magnetic resonance imaging patterns of muscle involvement in genetic muscle diseases: a systematic review.

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