Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease

• Published in: Frontiers in aging neuroscience Vol. 11; p. 167
• Main Authors: Cheng, Zenghui, Zhang, Jiping, He, Naying, Li, Yan, Wen, Yaofeng, Xu, Hongmin, Tang, Rongbiao, Jin, Zhijia, Haacke, E Mark, Yan, Fuhua, Qian, Dahong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 16-07-2019; Frontiers Media S.A
• Subjects: Accuracy; Artificial intelligence; Biomarkers; Diagnosis; Iron; Machine learning; Magnetic resonance imaging; Mapping; Medical prognosis; Mental disorders; Movement disorders; Neurodegenerative diseases; Neuroscience; nigrosome-1; NMR; Nuclear magnetic resonance; Parkinson's disease; quantitative susceptibility mapping; Radiomics; Red nucleus; Substantia nigra; support vector machine; United States > US; China; radiomics; nigrosome-1; quantitative susceptibility mapping; substantia nigra; Parkinson's disease; support vector machine
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2019.00167

The loss of nigrosome-1, which is also referred to as the swallow tail sign (STS) in T2
-weighted iron-sensitive magnetic resonance imaging (MRI), has recently emerged as a new biomarker for idiopathic Parkinson's disease (IPD). However, consistent recognition of the STS is difficult due to individual variations and different imaging parameters. Radiomics might have the potential to overcome these shortcomings. Therefore, we chose to explore whether radiomic features of nigrosome-1 of substantia nigra (SN) based on quantitative susceptibility mapping (QSM) could help to differentiate IPD patients from healthy controls (HCs).
Three-dimensional multi-echo gradient-recalled echo images (0.86 × 0.86 × 1.00 mm
) were obtained at 3.0-T MRI for QSM in 87 IPD patients and 77 HCs. Regions of interest (ROIs) of the SN below the red nucleus were manually drawn on both sides, and subsequently, volumes of interest (VOIs) were segmented (these ROIs included four 1-mm slices). Then, 105 radiomic features (including 18 first-order features, 13 shape features, and 74 texture features) of bilateral VOIs in the two groups were extracted. Forty features were selected according to the ensemble feature selection method, which combined analysis of variance, random forest, and recursive feature elimination. The selected features were further utilized to distinguish IPD patients from HC using the SVM classifier with 10 rounds of 3-fold cross-validation. Finally, the representative features were analyzed using an unpaired
-test with Bonferroni correction and correlated with the UPDRS-III scores.
The classification results from SVM were as follows: area under curve (AUC): 0.96 ± 0.02; accuracy: 0.88 ± 0.03; sensitivity: 0.89 ± 0.06; and specificity: 0.87 ± 0.07. Five representative features were selected to show their detailed difference between IPD patients and HCs: 10th percentile and median in IPD patients were higher than those in HCs (all
< 0.00125), while Gray Level Run Length Matrix (GLRLM)-Long Run Low Gray Level Emphasis, Gray Level Size Zone Matrix (GLSZM)-Gray Level Non-Uniformity, and volume (all
< 0.00125) in IPD patients were lower than those in HCs. The 10th percentile was positively correlated with UPDRS-III score (
= 0.35,
= 0.001).
Radiomic features of the nigrosome-1 region of SN based on QSM could be useful in the diagnosis of IPD and could serve as a surrogate marker for the STS.