Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China.
Neuroimage Clin. 2019;24:102070. doi: 10.1016/j.nicl.2019.102070. Epub 2019 Nov 5.
Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron deposition in the substantia nigra (SN) are associated with the pathophysiology. As an imaging technique that can quantitatively reflect the amount of iron deposition, Quantitative Susceptibility Mapping (QSM) has been shown to be a promising modality for the diagnosis of PD. In the present work, we propose a hybrid feature extraction method for PD diagnosis using QSM images. First, we extract radiomics features from the SN using QSM and employ machine learning algorithms to classify PD and normal controls (NC). This approach allows us to investigate which features are most vulnerable to the effects of the disease. Along with this approach, we propose a Convolutional Neural Network (CNN) based method which can extract different features from the QSM image to further support the diagnosis of PD. Finally, we combine these two types of features and we find that the radiomics features and CNN features are complementary to each other, which helps further improve the classification (diagnostic) performance. We conclude that: (1) radiomics features from QSM data have significant clinical value for the diagnosis of PD; (2) CNN features are also useful in the diagnosis of PD; and (3) the combination of radiomics features and CNN features can enhance the diagnostic accuracy.
帕金森病是仅次于阿尔茨海默病的老年人第二常见的神经退行性疾病。帕金森病（PD）的病因和发病机制尚不清楚，但黑质（SN）中多巴胺能细胞的丧失和过量的铁沉积与病理生理学有关。作为一种可以定量反映铁沉积量的成像技术，定量磁敏感图（QSM）已被证明是诊断 PD 的一种很有前途的方法。在本工作中，我们提出了一种使用 QSM 图像进行 PD 诊断的混合特征提取方法。首先，我们从 SN 中提取 QSM 图像的放射组学特征，并使用机器学习算法对 PD 和正常对照（NC）进行分类。这种方法可以让我们研究哪些特征对疾病的影响最敏感。除了这种方法，我们还提出了一种基于卷积神经网络（CNN）的方法，该方法可以从 QSM 图像中提取不同的特征，以进一步支持 PD 的诊断。最后，我们将这两种类型的特征结合起来，发现放射组学特征和 CNN 特征是互补的，这有助于进一步提高分类（诊断）性能。我们得出结论：（1）来自 QSM 数据的放射组学特征对 PD 的诊断具有重要的临床价值；（2）CNN 特征在 PD 诊断中也很有用；（3）放射组学特征和 CNN 特征的结合可以提高诊断准确性。
