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黑质黑质小体1区的放射组学特征:利用定量磁化率成像辅助特发性帕金森病的诊断。

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

作者信息

Cheng Zenghui, Zhang Jiping, He Naying, Li Yan, Wen Yaofeng, Xu Hongmin, Tang Rongbiao, Jin Zhijia, Haacke E Mark, Yan Fuhua, Qian Dahong

机构信息

Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

出版信息

Front Aging Neurosci. 2019 Jul 16;11:167. doi: 10.3389/fnagi.2019.00167. eCollection 2019.

Abstract

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.

摘要

在T2加权铁敏感磁共振成像(MRI)中,黑质小体-1的缺失,也被称为燕尾征(STS),最近已成为特发性帕金森病(IPD)的一种新生物标志物。然而,由于个体差异和不同的成像参数,对STS的一致识别较为困难。放射组学可能有潜力克服这些缺点。因此,我们选择探讨基于定量磁化率映射(QSM)的黑质(SN)黑质小体-1的放射组学特征是否有助于区分IPD患者和健康对照(HCs)。在3.0-T MRI上,对87例IPD患者和77例HCs进行QSM检查,获取三维多回波梯度回波图像(0.86×0.86×1.00 mm)。在双侧手动绘制红核下方SN的感兴趣区域(ROIs),随后分割感兴趣体积(VOIs)(这些ROIs包括四个1-mm切片)。然后,提取两组双侧VOIs的105个放射组学特征(包括18个一阶特征、13个形状特征和74个纹理特征)。根据综合特征选择方法选择40个特征,该方法结合了方差分析、随机森林和递归特征消除。使用支持向量机(SVM)分类器,通过10轮3折交叉验证,进一步利用所选特征区分IPD患者和HC。最后,使用Bonferroni校正的非配对t检验分析代表性特征,并与统一帕金森病评定量表第三部分(UPDRS-III)评分相关。SVM的分类结果如下:曲线下面积(AUC):0.96±0.02;准确率:0.88±0.03;灵敏度:0.89±0.06;特异性:0.87±0.07。选择五个代表性特征以显示IPD患者和HCs之间的详细差异:IPD患者的第10百分位数和中位数高于HCs(均P<0.00125),而IPD患者的灰度游程长度矩阵(GLRLM)-长游程低灰度强调、灰度大小区域矩阵(GLSZM)-灰度不均匀性和体积(均P<0.00125)低于HCs。第10百分位数与UPDRS-III评分呈正相关(r=0.35,P=0.001)。基于QSM的SN黑质小体-1区域的放射组学特征可用于IPD的诊断,并可作为STS的替代标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab4/6648885/4c0ac5baaede/fnagi-11-00167-g0001.jpg

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