诊断学理论与实践 ›› 2017, Vol. 16 ›› Issue (02): 147-151.doi: 10.16150/j.1671-2870.2017.02.005

• 论著 • 上一篇    下一篇

基于定量磁化率图像观察帕金森病患者黑质体-1退变的研究

贺娜英a, 许洪敏a, 黄沛b, 陈生弟b, 严福华a, 凌华威a   

  1. 上海交通大学医学院附属瑞金医院a. 放射科; b. 神经内科,上海 200025
  • 收稿日期:2017-04-13 出版日期:2017-04-25 发布日期:2017-04-25
  • 通讯作者: 凌华威 E-mail: noreply@netlogmail.com

Study of nigrosome-1 degeneration on imaging by quantitative susceptibility mapping in Parkinson's disease patients

HE Nayinga, XU Hongmina, HUANG Peib, CHEN Shengdib, YAN Fuhuaa, LING Huaweia   

  1. a. Department of Radio-logy, b. Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  • Received:2017-04-13 Online:2017-04-25 Published:2017-04-25

摘要: 目的 分析基于定量磁化率图像(quantitative susceptibility mapping, QSM)上黑质体-1结构的退变情况(即可见度)诊断帕金森病(Parkinson′s disease, PD)的诊断效能。方法 本研究为回顾性研究,共纳入2个被试群体。被试群体Ⅰ,共入组44例早期原发性PD患者和35名性别、年龄匹配的健康志愿者对照,于3T MRI上采集三维多回波GRE序列。被试群体Ⅱ,共入组43例原发性PD患者和48名性别、年龄相匹配的健康志愿者对照,于3T MRI上采集三维多回波GRE序列。基于梯度回波序列数据重建QSM。由2名放射科医师在不了解被试者分组情况下,基于QSM图像独立评判黑质体-1结构征象可见度,分为可见、疑似可见、不可见3种类型,根据双侧黑质体-1结构征象可见度将被试分为正常组、PD组和诊断不确定组。最后以被试者的临床诊断为金标准,计算基于黑质体-1结构征象的测评结果评价QSM图像诊断PD的灵敏度、特异度、阳性预测率、阴性预测率和准确率。结果 在被试群体Ⅰ中,42例PD患者诊断准确,10名健康志愿者诊断准确,基于黑质体-1结构征象诊断PD的灵敏度为95.5%,特异度为28.6%,阳性预测率为65.6%,阴性预测率为100%,准确率为66.0%。在被试群体Ⅱ中,42例PD患者诊断准确,29名健康志愿者诊断准确,基于黑质体-1结构征象诊断PD的灵敏度为97.7%,特异度为60.4%,阳性预测率为75.0%,阴性预测率为100%,准确率为78.0%。结论 QSM图像可被用于观察PD患者黑质体-1结构退变情况,基于QSM图像上的黑质体-1结构征象诊断PD患者具有较高的阴性预测率。

关键词: 帕金森病, 黑质体-1, 定量磁化率图像

Abstract: Objectives: To assess the diagnostic performance of nigrosome-1 degeneration on imaging by quantitative susceptibility mapping (QSM) for Parkinson's Disease (PD). Methods: Two cohorts (Cohort Ⅰ and Cohort Ⅱ) of patients were analyzed retrospectively in this study. For Cohort Ⅰ, forty-four early idiopathic PD patients and 35 age-and gender- matched healthy controls were recruited,and a three-dimensional 8-echo GRE sequence was acquired on a 3T MR scanner. For Cohort Ⅱ, 43 patients with idiopathic PD and 48 healthy gender- and age-matched controls were recruited. For each subject, a three-dimensional 16-echo GRE sequence was scanned. Then QSM was reconstructed from GRE data for all the subjects. Two neuro-radiologists who were blind to the diagnosis evaluated separately the visibility of nigrosome-1 on quantitative susceptibility mapping. There were three presenting types: present, possible present, and absent. Then all the subjects were classified into three groups based on the visibility of nigrosome-1: normal group, PD group, and non-diagnostic group. Clinical diagnosis was taken as the gold standard, and diagnostic performance of QSM for PD was assessed. Results: For Cohort Ⅰ, 42/44 PD and 10/35 controls were correctly classified, while there were 42/43 PD and 29/48 controls correctly classified in Cohort Ⅱ. Sensitivity of nigrosome-1 degeneration on imaging by QSM for diagnosing PD was 95.5%, specificity was 28.6%, positive predictive value (PPV) was 65.6%, negative predictive value (NPV) was 100% and accuracy rate was 66.0% in Cohort Ⅰ. While for cohort Ⅱ, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy rate were 97.7%, 60.4%, 75.0%, 100 % and 78.0%, respectively. Conclusions: QSM imaging could be used as a diagnostic tool for PD, which is feasible to assess the nigrosome-1 degeneration with a high negative predictive value.

Key words: Parkinson′ disease, Nigrosome-1, Quantitative susceptibility mapping

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