Department of Medical Imaging, Affiliated Hospital of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Department of Nuclear Medicine, Affiliated Hospital of Nantong University, Nantong, People's Republic of China.
Eur Radiol. 2022 Oct;32(10):6992-7003. doi: 10.1007/s00330-022-08790-8. Epub 2022 Apr 24.
To explore whether magnetic susceptibility value (MSV) and radiomics features of the nigrostriatal system could be used as imaging markers for diagnosing Parkinson's disease (PD) and its related cognitive impairment (CI).
A total of 104 PD patients and 45 age-sex-matched healthy controls (HCs) underwent quantitative susceptibility mapping (QSM). The former completed Hoehn-Yahr Stage and Montreal Cognitive Assessment (MoCA). The patients were divided into several subgroups according to disease stages, courses, and MoCA scores. The ROI was subdivided into the substantia nigra (SN), head of caudate nucleus (HCN), and putamen. The MSVs and radiomics features were obtained from QSM. The multivariable logistic regression (MLR) and support vector machine (SVM) models were constructed to diagnose PD. The correlations between MSVs, radiomics features, and MoCA scores were evaluated.
The MSVs in bilateral SN pars compacta (SNc) of PD patients were higher than those of the HCs (p < 0.001). There were differences in some radiomics features between the two groups (p < 0.05). The MSVs of the right SNc and the radiomics features of the right SN had the highest area under the curve (AUC), respectively. The comprehensive MLR model (0.90) and SVM model (0.95) revealed better classification performance than MSVs (p < 0.05) in diagnosing PD. The MSVs from the HCN were negatively correlated with MoCA scores in PD subgroups. There were correlations between radiomics features and MoCA scores in PD patients.
Radiomics features and MSVs of the nigrostriatal system from QSM could have crucial role in diagnosing PD and assessing CI.
• The MLR and the SVM models have excellent diagnostic performance in the diagnosis of PD. • A PD diagnostic nomogram, created based on MSV and the radiomics scores of SVM model, is very convenient for clinical use. • The radiomics features of the nigrostriatal system based on QSM help to evaluate the cognitive impairment in PD patients.
探讨黑质纹状体系统的磁化率值(MSV)和放射组学特征是否可作为诊断帕金森病(PD)及其相关认知障碍(CI)的影像学标志物。
共纳入 104 例 PD 患者和 45 名年龄、性别匹配的健康对照者(HCs)进行定量磁化率成像(QSM)检查。前者完成 Hoehn-Yahr 分期和蒙特利尔认知评估(MoCA)检查。根据疾病分期、病程和 MoCA 评分将患者分为多个亚组。ROI 分为黑质(SN)、尾状核头部(HCN)和壳核。从 QSM 中获取 MSV 和放射组学特征。采用多变量逻辑回归(MLR)和支持向量机(SVM)模型诊断 PD。评估 MSV、放射组学特征与 MoCA 评分的相关性。
PD 患者双侧 SN 致密部(SNc)的 MSV 高于 HCs(p<0.001)。两组间存在部分放射组学特征差异(p<0.05)。右侧 SNc 的 MSV 和右侧 SN 的放射组学特征的曲线下面积(AUC)最高。综合 MLR 模型(0.90)和 SVM 模型(0.95)在诊断 PD 中的分类性能均优于 MSV(p<0.05)。PD 亚组中,HCN 的 MSV 与 MoCA 评分呈负相关。PD 患者的放射组学特征与 MoCA 评分相关。
QSM 得到的黑质纹状体系统的放射组学特征和 MSV 对 PD 的诊断和 CI 的评估有重要作用。
· MLR 和 SVM 模型在 PD 诊断中具有出色的诊断性能。
· 基于 MSV 和 SVM 模型放射组学评分构建的 PD 诊断列线图,临床应用非常方便。
· QSM 得到的黑质纹状体系统的放射组学特征有助于评估 PD 患者的认知障碍。