Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

Zhang, Minghuan; Ma, Junshan; Shen, Ying; Chen, Ying

doi:10.1007/s11548-015-1312-0

Cited by 9 publications

(14 citation statements)

References 39 publications

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“…Radiomics, or the extraction of quantitative data based on the gray‐level intensity of multiple images, is increasingly being used by imaging research teams in various diseases, including the muscular dystrophies . Radiomic techniques include texture analysis, which produces quantitative metrics of radiologic images by using first‐ and second‐order statistics .…”

Section: Discussionmentioning

confidence: 99%

“…110,111 Radiomics, or the extraction of quantitative data based on the graylevel intensity of multiple images, is increasingly being used by imaging research teams in various diseases, including the muscular dystrophies. 112,113 Radiomic techniques include texture analysis, which produces quantitative metrics of radiologic images by using first-and second-order statistics. 98,114,115 These techniques can be used to calculate entropy (a measure of heterogeneity) or uniformity (a measure of gray-level intensity) within an image to give an idea of the "smoothness"…”

Section: Discussionmentioning

confidence: 99%

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Advancements in magnetic resonance imaging‐based biomarkers for muscular dystrophy

Leung

2019

Muscle and Nerve

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Recent years have seen steady progress in the identification of genetic muscle diseases as well as efforts to develop treatment for these diseases. Consequently, sensitive and objective new methods are required to identify and monitor muscle pathology. Magnetic resonance imaging offers multiple potential biomarkers of disease severity in the muscular dystrophies. This Review uses a pathology‐based approach to examine the ways in which MRI and spectroscopy have been used to study muscular dystrophies. Methods that have been used to quantitate intramuscular fat, edema, fiber orientation, metabolism, fibrosis, and vascular perfusion are examined, and this Review describes how MRI can help diagnose these conditions and improve upon existing muscle biomarkers by detecting small increments of disease‐related change. Important challenges in the implementation of imaging biomarkers, such as standardization of protocols and validating imaging measurements with respect to clinical outcomes, are also described.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Advancements in magnetic resonance imaging‐based biomarkers for muscular dystrophy

Leung

2019

Muscle and Nerve

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“…Our findings were generally in keeping with the heterogeneous nature of GRMD histopathologic changes and our prior MRI studies. Initial studies of heterogeneity focused on either T1w or T2w MRI images to differentiate between diseased and healthy subjects in clinical studies . As an example, Zhang et al used wavelet‐based features and a soft‐margin support vector machines (SVM) approach to assess heterogeneity of T1w and T2w MRI images.…”

Section: Discussionmentioning

confidence: 99%

“…Initial studies of heterogeneity focused on either T1w or T2w MRI images to differentiate between diseased and healthy subjects in clinical studies . As an example, Zhang et al used wavelet‐based features and a soft‐margin support vector machines (SVM) approach to assess heterogeneity of T1w and T2w MRI images. They differentiated healthy subjects and DMD patients with a classification accuracy of 92.9%.…”

Section: Discussionmentioning

confidence: 99%

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Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy

et al. 2019

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Introduction: Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. Methods: To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. Results: A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. Conclusions: The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes.Muscle Nerve 59: [380][381][382][383][384][385][386] 2019 Duchenne muscular dystrophy (DMD) is an X-Additional supporting information may be found in the online version of this article.

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Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review

et al. 2021

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There is a great demand for accurate non-invasive measures to better define the natural history of disease progression or treatment outcome in Duchenne muscular dystrophy (DMD) and to facilitate the inclusion of a large range of participants in DMD clinical trials. This review aims to investigate which MRI sequences and analysis methods have been used and to identify future needs. Medline, Embase, Scopus, Web of Science, Inspec, and Compendex databases were searched up to 2 November 2019, using keywords "magnetic resonance imaging" and "Duchenne muscular dystrophy." The review showed the trend of using T1w and T2w MRI images for semi-qualitative inspection of structural alterations of DMD muscle using a diversity of grading scales, with increasing use of T2map, Dixon, and MR spectroscopy (MRS).High-field (>3T) MRI dominated the studies with animal models. The quantitative MRI techniques have allowed a more precise estimation of local or generalized disease severity. Longitudinal studies assessing the effect of an intervention have also become more prominent, in both clinical and animal model subjects. Quality assessment of the included longitudinal studies was performed using the Newcastle-Ottawa Quality Assessment Scale adapted to comprise bias in selection, comparability, exposure, and outcome. Additional large clinical trials are needed to consolidate research using MRI as a biomarker in DMD and to validate findings against established gold standards. This future work should use a multiparametric and quantitative MRI acquisition protocol, assess the repeatability of measurements, and correlate findings to histologic parameters.

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Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

Abstract: The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Cited by 9 publications

References 39 publications

Advancements in magnetic resonance imaging‐based biomarkers for muscular dystrophy

Advancements in magnetic resonance imaging‐based biomarkers for muscular dystrophy

Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy

Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review

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