Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Schöpfstrasse 41/2, A-6020, Innsbruck, Austria.
Eur J Nucl Med Mol Imaging. 2011 Apr;38(4):702-10. doi: 10.1007/s00259-010-1681-0. Epub 2010 Dec 21.
The purpose of this study was to develop an observer-independent algorithm for the correct classification of dopamine transporter SPECT images as Parkinson's disease (PD), multiple system atrophy parkinson variant (MSA-P), progressive supranuclear palsy (PSP) or normal.
A total of 60 subjects with clinically probable PD (n = 15), MSA-P (n = 15) and PSP (n = 15), and 15 age-matched healthy volunteers, were studied with the dopamine transporter ligand [(123)I]β-CIT. Parametric images of the specific-to-nondisplaceable equilibrium partition coefficient (BP(ND)) were generated. Following a voxel-wise ANOVA, cut-off values were calculated from the voxel values of the resulting six post-hoc t-test maps. The percentages of the volume of an individual BP(ND) image remaining below and above the cut-off values were determined. The higher percentage of image volume from all six cut-off matrices was used to classify an individual's image. For validation, the algorithm was compared to a conventional region of interest analysis.
The predictive diagnostic accuracy of the algorithm in the correct assignment of a [(123)I]β-CIT SPECT image was 83.3% and increased to 93.3% on merging the MSA-P and PSP groups. In contrast the multinomial logistic regression of mean region of interest values of the caudate, putamen and midbrain revealed a diagnostic accuracy of 71.7%.
In contrast to a rater-driven approach, this novel method was superior in classifying [(123)I]β-CIT-SPECT images as one of four diagnostic entities. In combination with the investigator-driven visual assessment of SPECT images, this clinical decision support tool would help to improve the diagnostic yield of [(123)I]β-CIT SPECT in patients presenting with parkinsonism at their initial visit.
本研究旨在开发一种观察者独立的算法，正确分类多巴胺转运体 SPECT 图像为帕金森病（PD）、多系统萎缩帕金森变异型（MSA-P）、进行性核上性麻痹（PSP）或正常。
共纳入 60 例临床疑似 PD（n=15）、MSA-P（n=15）和 PSP（n=15）患者及 15 名年龄匹配的健康志愿者，使用多巴胺转运体配体[123I]β-CIT 进行研究。生成特异性与非置换平衡分区系数（BP（ND）的参数图像。在进行体素方差分析后，从六个事后 t 检验图的体素值计算出临界值。确定个体 BP（ND）图像中低于和高于临界值的体积百分比。使用所有六个临界值矩阵的较高图像体积百分比来分类个体的图像。为了验证，将该算法与传统的感兴趣区分析进行了比较。
该算法在正确分配[123I]β-CIT SPECT 图像方面的预测诊断准确性为 83.3%，将 MSA-P 和 PSP 组合并后提高至 93.3%。相比之下，尾状核、壳核和中脑的平均感兴趣区值的多项逻辑回归显示诊断准确性为 71.7%。
与基于评分者的方法相比，这种新方法在分类[123I]β-CIT-SPECT 图像为四种诊断实体之一方面具有优势。与 SPECT 图像的观察者驱动的视觉评估相结合，这种临床决策支持工具将有助于提高初始就诊帕金森病患者[123I]β-CIT SPECT 的诊断产量。
