Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach. Issue 37 (September 2018)

• Record Type: Journal Article
• Authors: Shao, Xiao-Ning; Sun, Ying-Jie; Xiao, Kun-Tao; Zhang, Yong; Zhang, Wen-Bo; Kou, Zhi-Feng; Cheng, Jing-Liang
• Other Names: Zhang. Heye section editor.
• Is Part Of: Medicine. Volume 97:Issue 37(2018)
• Publication Date: 2018-09
• Subjects: cardiovascular magnetic resonance (CMR) -- dilated Cardiomyopathy (DCM) -- support vector machine (SVM) -- T1 mapping -- texture analysis (TA); Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000012246
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM. A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist. Twelve histogram parameters and 5 gray-level co-occurrence matrix (GLCM) features were extracted during the TA. Differences in texture features between DCM patients and healthy controls were evaluated by t test. Support vector machine (SVM) was used to calculate the diagnostic accuracy of those texture parameters. Most histogram features were higher in the DCM group when compared to healthy controls, and 9 of these had significant differences between the DCM group and healthy controls. In terms of GLCM features, energy, correlation, and homogeneity were higher in the DCM group, when compared with healthy controls. In addition, entropy and contrast were lower in the DCM group. Moreover, entropy, contrast, and homogeneity had significant differences between these 2 groups. The diagnostic accuracy when using the SVM classifier with all these histogram and GLCM features was 0.85 ± 0.07. A computer-based TA and machine learning approach of T1 mapping can provide an objective tool for the diagnosis of DCM.