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

Abstract To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyosit...

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Autores principales:	Keita Nagawa, Masashi Suzuki, Yuuya Yamamoto, Kaiji Inoue, Eito Kozawa, Toshihide Mimura, Koichiro Nakamura, Makoto Nagata, Mamoru Niitsu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7689333e8165491185bd5743bb28d6bc
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spelling	oai:doaj.org-article:7689333e8165491185bd5743bb28d6bc2021-12-02T15:36:31ZTexture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies10.1038/s41598-021-89311-32045-2322https://doaj.org/article/7689333e8165491185bd5743bb28d6bc2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89311-3https://doaj.org/toc/2045-2322Abstract To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646–0.853 and 0.692–0.792, with accuracy of 71.5–81.0 and 65.8–78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.Keita NagawaMasashi SuzukiYuuya YamamotoKaiji InoueEito KozawaToshihide MimuraKoichiro NakamuraMakoto NagataMamoru NiitsuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Keita Nagawa Masashi Suzuki Yuuya Yamamoto Kaiji Inoue Eito Kozawa Toshihide Mimura Koichiro Nakamura Makoto Nagata Mamoru Niitsu Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
description	Abstract To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646–0.853 and 0.692–0.792, with accuracy of 71.5–81.0 and 65.8–78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.
format	article
author	Keita Nagawa Masashi Suzuki Yuuya Yamamoto Kaiji Inoue Eito Kozawa Toshihide Mimura Koichiro Nakamura Makoto Nagata Mamoru Niitsu
author_facet	Keita Nagawa Masashi Suzuki Yuuya Yamamoto Kaiji Inoue Eito Kozawa Toshihide Mimura Koichiro Nakamura Makoto Nagata Mamoru Niitsu
author_sort	Keita Nagawa
title	Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
title_short	Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
title_full	Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
title_fullStr	Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
title_full_unstemmed	Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies
title_sort	texture analysis of muscle mri: machine learning-based classifications in idiopathic inflammatory myopathies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7689333e8165491185bd5743bb28d6bc
work_keys_str_mv	AT keitanagawa textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT masashisuzuki textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT yuuyayamamoto textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT kaijiinoue textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT eitokozawa textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT toshihidemimura textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT koichironakamura textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT makotonagata textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies AT mamoruniitsu textureanalysisofmusclemrimachinelearningbasedclassificationsinidiopathicinflammatorymyopathies
_version_	1718386297023758336

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

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