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.
Highlights
Abbreviations amyopathic dermatomyositis (ADM) Amyopathic dermatomyositis anti-synthetase syndrome (ASS) Anti-synthetase syndrome area under the curve (AUC) Area under the curve DM Dermatomyositis GLCM Gray-level co-occurrence matrix GLDM Gray-level dependence matrix GLRLM Gray-level run-length matrix GLZLM Gray-level zone length matrix high signal intensity (HSI) High signal intensity inclusion body myositis (IBM) Inclusion body myositis intraclass correlation coefficient (ICC) Intraclass correlation coefficient
We found that machine learning (ML)-based texture analysis (TA) of muscle MRI has the potential to distinguish between PM, DM, and ADM
ML models distinguishing between non-Idiopathic inflammatory myopathies (IIMs) and IIM disease groups had low classification accuracy
Summary
Abbreviations ADM Amyopathic dermatomyositis ASS Anti-synthetase syndrome AUC Area under the curve DM Dermatomyositis GLCM Gray-level co-occurrence matrix GLDM Gray-level dependence matrix GLRLM Gray-level run-length matrix GLZLM Gray-level zone length matrix HSI High signal intensity IBM Inclusion body myositis ICC Intraclass correlation coefficient. The common disease groups of IIMs in adults are polymyositis (PM), dermatomyositis (DM), amyopathic dermatomyositis (ADM), and inclusion body myositis (IBM). These inflammatory myopathies show different clinical presentation patterns and responses to treatment[3,4,5,6]. A texture analysis (TA) is an image analysis technique that allows for the quantification of image characteristics based on the distribution of pixels and their surface intensity or p atterns[17,18]. To the best of our knowledge, an analysis of IIMs with texture features derived from muscle MRI has not yet been conducted
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