Department of Radiology, the Second Affiliated Hospital of Soochow University, Suzhou, China.
Department of Neurology, the Second Affiliated Hospital of Soochow University, Suzhou, China.
Acad Radiol. 2022 Mar;29 Suppl 3:S71-S79. doi: 10.1016/j.acra.2020.10.027. Epub 2020 Nov 12.
To investigate the diagnostic performance of histogram analysis combined with quantitative susceptibility mapping (QSM) for differentiating Parkinson's disease (PD) patients from healthy controls.
We included 35 patients with PD diagnosed by two neurologists from August 2019 to January 2020 in our hospital in this prospective study. The clinical diagnosis was based on the Movement Disorder Society Clinical Diagnostic Criteria for PD. At the same time, 23 healthy volunteers matched for age and sex were recruited as controls. The Mini Mental State Examination, the third part of the Parkinson's Disease Rating Scale, the Hoehn & Yahr stages, and disease duration (year) were used to assess the PD patients. QSM was performed using a 3T MR scanner. The regions of interest were depicted according to the head of the caudate nucleus(CN), globus pallidus(GP), putamina (PUT), thalmus(TH), substantia nigra (SN), red nucleus(RN), and dentate nucleus. Then the corresponding histogram features were extracted. The Mann-Whitney U test was used to identify significant histogram features for differentiating PD patients from healthy controls. Area under the receiver operating characteristics curve (AUC) analysis was conducted to evaluate the diagnostic performance of all significant histogram features. Multivariate logistic regression analysis was performed to identify the best combined model for all seven nuclei. Differences among the AUCs were compared pairwise.
Histogram features in all nuclei except TH showed significant differences between the groups. Among the single features, the 10th percentile of SN (SN) yielded the highest AUC of 0.894, with the highest specificity of 86.86% for differentiating PD patients from healthy controls. The 75th percentile of PUT (PUT) yielded the highest sensitivity of 97.14%. In the multivariate logistic regression analysis, SN combined with PUT yielded the highest diagnostic performance with the highest AUC of 0.911, the highest specificity of 91.30% and an excellent sensitivity of 92.40%.
QSM combined with histogram analysis successfully distinguished PD patients from healthy controls, and the result was notably superior to the mean value.
研究基于直方图分析联合定量磁化率映射（QSM）区分帕金森病（PD）患者和健康对照的诊断性能。
本前瞻性研究纳入了 2019 年 8 月至 2020 年 1 月我院收治的 35 例经两位神经科医生诊断为 PD 的患者。临床诊断基于运动障碍协会 PD 临床诊断标准。同时，招募了 23 名年龄和性别相匹配的健康志愿者作为对照。采用简易精神状态检查量表、帕金森病评定量表第三部分、Hoehn & Yahr 分期和病程（年）评估 PD 患者。采用 3T 磁共振扫描仪进行 QSM。根据尾状核头部（CN）、苍白球（GP）、壳核（PUT）、丘脑（TH）、黑质（SN）、红核（RN）和齿状核描绘感兴趣区，然后提取相应的直方图特征。采用 Mann-Whitney U 检验识别区分 PD 患者和健康对照的显著直方图特征。通过受试者工作特征曲线下面积（AUC）分析评估所有显著直方图特征的诊断性能。采用多变量逻辑回归分析确定所有 7 个核的最佳组合模型。对 AUC 之间的差异进行两两比较。
除 TH 外，所有核的直方图特征在两组间均有显著差异。在单特征中，SN 的第 10 百分位数（SN）的 AUC 最高为 0.894，对区分 PD 患者和健康对照的特异性最高为 86.86%。PUT 的第 75 百分位数（PUT）的敏感性最高为 97.14%。在多变量逻辑回归分析中，SN 与 PUT 联合的诊断性能最高，AUC 最高为 0.911，特异性最高为 91.30%，敏感性极佳为 92.40%。
QSM 联合直方图分析可成功区分 PD 患者和健康对照，其结果明显优于平均值。
