Literature DB >> 28156201

Neurogenic and Myogenic Diseases: Quantitative Texture Analysis of Muscle US Data for Differentiation.

Kazuki Sogawa1, Hiroyuki Nodera1, Naoko Takamatsu1, Atsuko Mori1, Hiroki Yamazaki1, Yoshimitsu Shimatani1, Yuishin Izumi1, Ryuji Kaji1.   

Abstract

Purpose To assess the multiple texture features of skeletal muscles in neurogenic and myogenic diseases by using ultrasonography (US). Materials and Methods After institutional review board approval, muscle US studies of the medial head of the gastrocnemius were performed prospectively in patients with neurogenic diseases (n = 25 [18 men]; mean age, 66.0 years ± 12.3 [standard deviation]), in patients with myogenic diseases (n = 21 [12 men]; mean age, 68.3 years ± 11.5), and in healthy control subjects (n = 21 [11 men]; mean age, 70.5 years ± 8.4) between January 2013 and May 2016. Written informed consent was obtained. Muscle texture parameters were obtained, and five algorithms were used to classify the groups. Results The neurogenic and myogenic disease groups showed higher echo intensities than the control subjects. The histogram-derived texture parameters had overlaps between the neurogenic and myogenic groups and thus had a low discrimination rate. With assessment of more classes of texture parameters, three groups were correctly classified (100% correct, according to four of five classification algorithms). Tenfold cross validation showed 93.5%-95.7% correct classification between the neurogenic and myogenic groups. The run-length matrix, autoregressive model, and co-occurrence matrix were particularly useful in distinguishing the neurogenic and myogenic groups. Conclusion Texture analysis of muscle US data can enable differentiation between neurogenic and myogenic diseases and is useful in noninvasively assessing underlying disease mechanisms. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28156201     DOI: 10.1148/radiol.2016160826

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  12 in total

1.  Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data.

Authors:  Manoj Mannil; Jakob M Burgstaller; Arjun Thanabalasingam; Sebastian Winklhofer; Michael Betz; Ulrike Held; Roman Guggenberger
Journal:  Skeletal Radiol       Date:  2018-03-01       Impact factor: 2.199

2.  Histogram analysis derived from apparent diffusion coefficient (ADC) is more sensitive to reflect serological parameters in myositis than conventional ADC analysis.

Authors:  Hans Jonas Meyer; Alexander Emmer; Malte Kornhuber; Alexey Surov
Journal:  Br J Radiol       Date:  2018-02-20       Impact factor: 3.039

Review 3.  A focused review of myokines as a potential contributor to muscle hypertrophy from resistance-based exercise.

Authors:  Stephen M Cornish; Eric M Bugera; Todd A Duhamel; Jason D Peeler; Judy E Anderson
Journal:  Eur J Appl Physiol       Date:  2020-03-06       Impact factor: 3.078

4.  Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

Authors:  Keita Nagawa; Masashi Suzuki; Yuuya Yamamoto; Kaiji Inoue; Eito Kozawa; Toshihide Mimura; Koichiro Nakamura; Makoto Nagata; Mamoru Niitsu
Journal:  Sci Rep       Date:  2021-05-10       Impact factor: 4.379

5.  The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation.

Authors:  Peng-Fei Yan; Ling Yan; Ting-Ting Hu; Dong-Dong Xiao; Zhen Zhang; Hong-Yang Zhao; Jun Feng
Journal:  Transl Oncol       Date:  2017-06-24       Impact factor: 4.243

6.  Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength.

Authors:  Michael Dieckmeyer; Stephanie Inhuber; Sarah Schlaeger; Dominik Weidlich; Muthu Rama Krishnan Mookiah; Karupppasamy Subburaj; Egon Burian; Nico Sollmann; Jan S Kirschke; Dimitrios C Karampinos; Thomas Baum
Journal:  Diagnostics (Basel)       Date:  2021-02-04

7.  Texture Analysis on Ultrasound: The Effect of Time Gain Compensation on Histogram Metrics and Gray-Level Matrices.

Authors:  Giulio Vara; Arianna Rustici; Andrea Sechi; Cristina Mosconi; Vincenzo Lucidi; Rita Golfieri
Journal:  J Med Phys       Date:  2021-02-02

8.  Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

Authors:  Philippe Burlina; Seth Billings; Neil Joshi; Jemima Albayda
Journal:  PLoS One       Date:  2017-08-30       Impact factor: 3.240

9.  Artificial intelligence in musculoskeletal ultrasound imaging.

Authors:  YiRang Shin; Jaemoon Yang; Young Han Lee; Sungjun Kim
Journal:  Ultrasonography       Date:  2020-09-06

Review 10.  Muscle ultrasound: Present state and future opportunities.

Authors:  Juerd Wijntjes; Nens van Alfen
Journal:  Muscle Nerve       Date:  2020-10-13       Impact factor: 3.217

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