Combinostics Ltd., Hatanpään valtatie 24, 33100, Tampere, Finland.
Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.
Eur Radiol. 2019 Sep;29(9):4937-4947. doi: 10.1007/s00330-019-06067-1. Epub 2019 Feb 22.
The aims of this study were to examine whether visual MRI rating scales used in diagnostics of cognitive disorders can be estimated computationally and to compare the visual rating scales with their computed counterparts in differential diagnostics.
A set of volumetry and voxel-based morphometry imaging biomarkers was extracted from T1-weighted and FLAIR images. A regression model was developed for estimating visual rating scale values from a combination of imaging biomarkers. We studied three visual rating scales: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and white matter hyperintensities (WMHs) measured by the Fazekas scale. Images and visual ratings from the Amsterdam Dementia Cohort (ADC) (N = 513) were used to develop the models and cross-validate them. The PredictND (N = 672) and ADNI (N = 752) cohorts were used for independent validation to test generalizability.
The correlation coefficients between visual and computed rating scale values were 0.83/0.78 (MTA-left), 0.83/0.79 (MTA-right), 0.64/0.64 (GCA), and 0.76/0.75 (Fazekas) in ADC/PredictND cohorts. When performance in differential diagnostics was studied for the main types of dementia, the highest balanced accuracy, 0.75-0.86, was observed for separating different dementias from cognitively normal subjects using computed GCA. The lowest accuracy of about 0.5 for all the visual and computed scales was observed for the differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Computed scales produced higher balanced accuracies than visual scales for MTA and GCA (statistically significant).
MTA, GCA, and WMHs can be reliably estimated automatically helping to provide consistent imaging biomarkers for diagnosing cognitive disorders, even among less experienced readers.
• Visual rating scales used in diagnostics of cognitive disorders can be estimated computationally from MRI images with intraclass correlations ranging from 0.64 (GCA) to 0.84 (MTA). • Computed scales provided high diagnostic accuracy with single-subject data (area under the receiver operating curve range, 0.84-0.94).
本研究旨在检验用于认知障碍诊断的视觉 MRI 评分量表是否可通过计算方法进行评估，并比较这些视觉评分量表与计算得出的评分量表在鉴别诊断中的差异。
从 T1 加权和液体衰减反转恢复（FLAIR）图像中提取了一组容积和基于体素的形态计量学成像生物标志物。我们建立了一个回归模型，用于从成像生物标志物组合中估算视觉评分量表的值。我们研究了三种视觉评分量表：内侧颞叶萎缩（MTA）、全脑皮质萎缩（GCA）和由 Fazekas 量表测量的脑白质高信号（WMHs）。使用阿姆斯特丹痴呆队列（ADC）（N=513）的图像和视觉评分来开发模型并进行交叉验证。PredictND（N=672）和 ADNI（N=752）队列用于独立验证以检验通用性。
ADC/PredictND 队列中视觉和计算评分量表值之间的相关系数分别为 0.83/0.78（左 MTA）、0.83/0.79（右 MTA）、0.64/0.64（GCA）和 0.76/0.75（Fazekas）。当研究鉴别诊断对主要类型痴呆的性能时，使用计算得出的 GCA 区分不同类型的痴呆与认知正常个体，其平衡准确性最高，为 0.75-0.86。所有视觉和计算量表中，区分阿尔茨海默病和额颞叶变性的准确性最低，约为 0.5。对于 MTA 和 GCA，计算得出的量表比视觉量表具有更高的平衡准确性（具有统计学意义）。
即使在经验较少的读者中，MTA、GCA 和 WMH 也可以通过 MRI 图像自动可靠地进行估算，从而有助于提供一致的成像生物标志物以诊断认知障碍。
• 用于认知障碍诊断的视觉评分量表可通过计算方法从 MRI 图像中估算得出，其组内相关系数（ICC）范围为 0.64（GCA）至 0.84（MTA）。• 基于个体数据的计算量表提供了较高的诊断准确性（接受者操作特征曲线下面积范围为 0.84-0.94）。
