Fang Eric, Ann Chu Ning, Maréchal Bénédicte, Lim Jie Xin, Tan Shawn Yan Zhi, Li Huihua, Gan Julian, Tan Eng King, Chan Ling Ling
Singapore General Hospital, Singapore.
National Neuroscience Institute, Singapore.
J Magn Reson Imaging. 2020 Mar;51(3):748-756. doi: 10.1002/jmri.26887. Epub 2019 Jul 31.
Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist.
To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype.
Prospective.
In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments.
FIELD STRENGTH/SEQUENCE: 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences.
Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently.
Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis.
Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84).
Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning.
1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:748-756.
脑室周围白质疏松可能是帕金森病（PD）运动亚型姿势性不稳步态障碍（PIGD）的重要病理变化。对于普通神经科医生而言，PIGD的临床诊断可能具有挑战性。
评估1）基于体积的全自动形态测量法（Vol-BM）在表征PD和PIGD成像诊断标志物方面的效用，包括2）新的深部灰质核病变负荷（GMab），以及3）Vol-BM模型构建在对PIGD亚型进行分类时的鉴别性能。
前瞻性研究。
总共23例PIGD患者、21例PD患者和20例年龄匹配的健康对照者（HC）接受了脑部MRI扫描和临床评估。
场强/序列：3.0T，矢状位三维磁化准备快速梯度回波（MPRAGE）序列和液体衰减反转恢复成像（FLAIR）序列。
由运动障碍神经科医生进行临床评估。然后使用自动多模态Vol-BM算法（MorphoBox）对脑部MR图像进行分割，并由两位作者独立审核。
分别使用方差分析（ANOVA）和回归分析评估脑部分割以及临床参数差异和相关性。进行逻辑回归以区分PIGD和PD，并使用受试者工作特征（ROC）分析评估鉴别可靠性。
与PD患者和HC相比，PIGD患者的白质病变负荷（WMab）（P < 0.01）、尾状核GMab（P < 0.05）以及侧脑室和第三脑室容积（P < 0.05）显著更高。在两组患者中，WMab、尾状核和壳核GMab以及尾状核、侧脑室和第三脑室容积在与平衡和步态评估的线性回归中显示出显著系数（P < 0.005）。一个包含WMab、尾状核GMab和尾状核GM的模型区分PIGD与PD和HC的灵敏度 = 0.83，特异度 = 0.76（曲线下面积 = 0.84）。
使用自动Vol-BM对PD和PIGD患者脑微结构变化进行快速、无偏倚的量化是可行的。白质和尾状核的复合病变负荷以及尾状核容积区分了PIGD与PD和HC，并在使用监督机器学习对这些疾病进行分类方面显示出潜力。
1 技术效能：1级 《磁共振成像杂志》2020年；51：748 - 756。
