Center for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Huntington's Disease Research Center, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom.
Ann Neurol. 2020 May;87(5):751-762. doi: 10.1002/ana.25709. Epub 2020 Mar 14.
The identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD.
We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers.
We identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers.
Our findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762.
鉴定敏感生物标志物对于验证亨廷顿病（HD）的治疗方法至关重要。我们直接比较最大的集体成像 HD 数据集的结构成像标志物，以确定一组对多中心变化具有稳健性的成像标志物，并为 HD 临床试验得出样本量的上限估计。
我们使用 1 个后处理管道来回顾性分析来自 PREDICT-HD、TRACK-HD 和 IMAGE-HD 研究的 624 名参与者在 3 个时间点的 T1 加权磁共振成像（MRI）扫描。我们使用混合效应模型调整区域脑容量的协变量，计算效应大小，并模拟疾病相关解剖区域的可能治疗效果。我们使用我们的模型来估计可能的治疗效果在解剖区域和临床标志物上的统计功效。
我们确定了一组常见的解剖区域，这些区域在健康对照组和前manifest HD（PreHD）组之间具有相似的大标准化效应大小（>0.5）。这些区域包括皮质下、白质和皮质区域以及非脑室性脑脊液（CSF）。我们还观察到整个大脑中效应大小的一致空间分布。我们发现，多中心研究对于捕捉治疗效果方差是必要的；对于 20%的治疗效果，尾状核（n=661）、苍白球（n=687）和非脑室性 CSF（n=939）的功效超过 80%，而且，这些影像学标志物提供了比标准临床标志物更大的功效。
我们的研究结果提供了在 HD 中首次对结构影像学标志物的跨研究验证，支持将这些测量作为观察性研究和临床试验的终点。ANN NEUROL 2020；87：751-762。
