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. 2019 Dec 19;1(5):e180026.
doi: 10.1148/ryct.2019180026. eCollection 2019 Dec.

Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis

Tommaso Di Noto  1 , Jochen von Spiczak  1 , Manoj Mannil  1 , Elena Gantert  1 , Paolo Soda  1 , Robert Manka  1 , Hatem Alkadhi  1
Affiliations

Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis

Tommaso Di Noto et al. Radiol Cardiothorac Imaging. .

Abstract

Purpose: To evaluate whether radiomics features of late gadolinium enhancement (LGE) regions at cardiac MRI enable distinction between myocardial infarction (MI) and myocarditis and to compare radiomics with subjective visual analyses by readers with different experience levels.

Materials and methods: In this retrospective, institutional review board-approved study, consecutive MRI examinations of 111 patients with MI and 62 patients with myocarditis showing LGE were included. By using open-source software, classification performances attained from two-dimensional (2D) and three-dimensional (3D) texture analysis, shape, and first-order descriptors were compared, applying five different machine learning algorithms. A nested, stratified 10-fold cross-validation was performed. Classification performances were compared through Wilcoxon signed-rank tests. Supervised and unsupervised feature selection techniques were tested; the effect of resampling MR images was analyzed. Subjective image analysis was performed on 2D and 3D image sets by two independent, blinded readers with different experience levels.

Results: When trained with recursive feature elimination (RFE), a support vector machine achieved the best results (accuracy: 88%) for 2D features, whereas linear discriminant analysis (LDA) showed the highest accuracy (85%) for 3D features (P <.05). When trained with principal component analysis (PCA), LDA attained the highest accuracy with both 2D (86%) and 3D (89%; P =.4) features. Results found for classifiers trained with spline resampling were less accurate than those achieved with one-dimensional (1D) nearest-neighbor interpolation (P <.05), whereas results for classifiers trained with 1D nearest-neighbor interpolation and without resampling were similar (P =.1). As compared with the radiomics approach, subjective visual analysis performance was lower for the less experienced and higher for the experienced reader for both 2D and 3D data.

Conclusion: Radiomics features of LGE permit the distinction between MI and myocarditis with high accuracy by using either 2D features and RFE or 3D features and PCA.© RSNA, 2019Supplemental material is available for this article.

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Conflict of interest statement

Disclosures of Conflicts of Interest: T.D.N. Activities related to the present article: author received grant from European School of Radiology (research fellowship organized by ESOR in many European hospitals). Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. J.v.S. disclosed no relevant relationships. M.M. disclosed no relevant relationships. E.G. disclosed no relevant relationships. P.S. disclosed no relevant relationships. R.M. disclosed no relevant relationships. H.A. disclosed no relevant relationships.

Figures

Figure 1:
Figure 1:
Schematic illustration of the radiomics and machine learning approach. 3D = three dimensional, 2D = two dimensional.
Figure 2a:
Figure 2a:
Images in a 74-year-old male patient with subacute myocardial infarction. (a) Late gadolinium enhancement cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the infarcted area. (c) Three-dimensional volume-of-interest segmentation of the infarcted area.
Figure 2b:
Figure 2b:
Images in a 74-year-old male patient with subacute myocardial infarction. (a) Late gadolinium enhancement cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the infarcted area. (c) Three-dimensional volume-of-interest segmentation of the infarcted area.
Figure 2c:
Figure 2c:
Images in a 74-year-old male patient with subacute myocardial infarction. (a) Late gadolinium enhancement cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the infarcted area. (c) Three-dimensional volume-of-interest segmentation of the infarcted area.
Figure 3a:
Figure 3a:
Images in a 24-year-old male patient with acute myocarditis. (a) Late gadolinium enhancement (LGE) cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the LGE area. (c) Three-dimensional volume-of-interest segmentation of the LGE area.
Figure 3b:
Figure 3b:
Images in a 24-year-old male patient with acute myocarditis. (a) Late gadolinium enhancement (LGE) cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the LGE area. (c) Three-dimensional volume-of-interest segmentation of the LGE area.
Figure 3c:
Figure 3c:
Images in a 24-year-old male patient with acute myocarditis. (a) Late gadolinium enhancement (LGE) cardiac MR image in the short-axis view. (b) Two-dimensional region-of-interest segmentation of the LGE area. (c) Three-dimensional volume-of-interest segmentation of the LGE area.
Figure 4:
Figure 4:
Graphical representation of the two-dimensional filtering process (preferred to three-dimensional filtering for simplicity of visualization). Features were computed from original images, from four wavelet-filtered sub-band images and from exponential-filtered images. In the red boxes within each filter, the results of the filters applied to the red box in the original image are shown. The dotted blue box summarizes how wavelet filtering decomposes the original image.
Figure 5:
Figure 5:
Bar charts show the accuracy, sensitivity, specificity, and precision (in percentages) of the two readers, reader 1 and reader 2, performing a subjective visual analysis of late gadolinium enhancement images and of the radiomics approach with both two-dimensional (2D) and three-dimensional (3D) image data. For the radiomics approach, 2D-derived support vector machine trained with recursive feature elimination and 3D-derived linear discriminant analysis trained with principal component analysis are shown.
See this image and copyright information in PMC

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