Mansoor Nina M, Vanniyasingam Tishok, Malone Ian, Hobbs Nicola Z, Rees Elin, Durr Alexandra, Roos Raymund A C, Landwehrmeyer Bernhard, Tabrizi Sarah J, Johnson Eileanoir B, Scahill Rachael I
Department of Neurodegenerative Disease, Huntington's Disease Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom.
Department of Neurodegenerative Disease, Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, United Kingdom.
Front Neurol. 2021 Apr 14;12:616272. doi: 10.3389/fneur.2021.616272. eCollection 2021.
Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric measures the current gold standard is manual delineation, which is unfeasible for samples sizes required for large clinical trials. Using a cohort of early Huntington's disease (HD) patients ( = 46) and controls ( = 35), we compared the performance of four automated segmentation tools (FIRST, FreeSurfer, STEPS, MALP-EM) with manual delineation for generating cross-sectional caudate volume, a region known to be vulnerable in HD. We then examined the effect of each of these baseline regions on the ability to detect change over 15 months using the established longitudinal Caudate Boundary Shift Integral (cBSI) method, an automated longitudinal pipeline requiring a baseline caudate region as an input. All tools, except Freesurfer, generated significantly smaller caudate volumes than the manually derived regions. Jaccard indices showed poorer levels of overlap between each automated segmentation and manual delineation in the HD patients compared with controls. Nevertheless, each method was able to demonstrate significant group differences in volume ( < 0.001). STEPS performed best qualitatively as well as quantitively in the baseline analysis. Caudate atrophy measures generated by the cBSI using automated baseline regions were largely consistent with those derived from a manually segmented baseline, with STEPS providing the most robust cBSI values across both control and HD groups. Atrophy measures from the cBSI were relatively robust to differences in baseline segmentation technique, suggesting that fully automated pipelines could be used to generate outcome measures for clinical trials.
神经影像学在为一系列神经退行性疾病的治疗试验生成敏感且客观的结果指标方面显示出巨大潜力。对于体积测量，当前的金标准是手动勾勒，这对于大型临床试验所需的样本量而言是不可行的。我们使用一组早期亨廷顿舞蹈病（HD）患者（n = 46）和对照组（n = 35），比较了四种自动分割工具（FIRST、FreeSurfer、STEPS、MALP - EM）与手动勾勒在生成横断面尾状核体积方面的性能，尾状核是HD中已知的易损区域。然后，我们使用既定的纵向尾状核边界位移积分（cBSI）方法，研究了这些基线区域中的每一个对检测15个月内变化能力的影响，cBSI是一种需要将基线尾状核区域作为输入的自动纵向流程。除了FreeSurfer之外，所有工具生成的尾状核体积均显著小于手动得出的区域。杰卡德指数显示，与对照组相比，HD患者中每种自动分割与手动勾勒之间的重叠水平较差。然而，每种方法都能够证明在体积上存在显著的组间差异（p < 0.001）。在基线分析中，STEPS在定性和定量方面表现最佳。使用自动基线区域通过cBSI生成的尾状核萎缩测量结果与从手动分割基线得出的结果基本一致，STEPS在对照组和HD组中均提供了最可靠的cBSI值。cBSI得出的萎缩测量结果对基线分割技术的差异相对稳健，这表明全自动流程可用于生成临床试验的结果指标。
