MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study

Huize Pang; Ziyang Yu; Renyuan Li; Huaguang Yang; Guoguang Fan

doi:10.3389/fnagi.2020.587250

MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study

Front Aging Neurosci. 2020 Nov 12:12:587250. doi: 10.3389/fnagi.2020.587250. eCollection 2020.

Authors

Huize Pang¹, Ziyang Yu², Renyuan Li^{3

4}, Huaguang Yang¹, Guoguang Fan¹

Affiliations

¹ Department of Radiology, The first affiliated hospital of China Medical University, China Medical University, Shenyang, China.
² School of Medicine, Xiamen University, Xiamen, China.
³ Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
⁴ The Affiliated Sir Run Run Shaw hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Abstract

Objectives: To investigate the value of MRI-based radiomic model based on the radiomic features of different basal nuclei in differentiating idiopathic Parkinson's disease (IPD) from Parkinsonian variants of multiple system atrophy (MSA-P).

Methods: Radiomics was applied to the 3T susceptibility- weighted imaging (SWI) from 102 MSA-P patients and 83 IPD patients (allocated to a training and a testing cohort, 7:3 ratio). The substantia nigra (SN), caudate nucleus (CN), putamen (PUT), globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented, and 396 features were extracted. After feature selection, support vector machine (SVM) was generated, and its predictive performance was calculated in both the training and testing cohorts using the area under receiver operating characteristic curve (AUC).

Results: Seven radiomic features were selected from the PUT, by which the SVM classifier achieved the best diagnostic performance with an AUC of 0.867 in the training cohort and an AUC of 0.862 in the testing cohort. Furthermore, the combined model, which incorporating part III of the Parkinson's Disease Rating Scale (UPDRSIII) scores into radiomic features of the PUT, further improved the diagnostic performance. However, radiomic features extracted from RN, SN, GP, CN, and STN had moderate to poor diagnostic performance, with AUC values that ranged from 0.610 to 0.788 in the training cohort and 0.583 to 0.766 in the testing cohort.

Conclusion: Radiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.

Keywords: idiopathic Parkinson’s disease; multiple system atrophy; radiomics; support vector machine; susceptibility weighted imaging.