在一项多中心临床常规真实世界研究中，通过人工智能测量的丘脑萎缩与残疾进展相关。
Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-word study is associated with disability progression.
作者信息
Zivadinov Robert, Bergsland Niels, Jakimovski Dejan, Weinstock-Guttman Bianca, Benedict Ralph H B, Riolo Jon, Silva Diego, Dwyer Michael G
机构信息
Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, USA
Center for Biomedical Imaging at Clinical and Translational Science Institute, University of Buffalo, State University of New York, Buffalo, New York, USA.
出版信息
J Neurol Neurosurg Psychiatry. 2022 Jul 28. doi: 10.1136/jnnp-2022-329333.
BACKGROUND
The thalamus is a key grey matter structure, and sensitive marker of neurodegeneration in multiple sclerosis (MS). Previous reports indicated that thalamic volumetry using artificial intelligence (AI) on clinical-quality T2-fluid-attenuated inversion recovery (FLAIR) images alone is fast and reliable.
OBJECTIVE
To investigate whether thalamic volume (TV) loss, measured longitudinally by AI, is associated with disability progression (DP) in patients with MS, participating in a large multicentre study.
METHODS
The DeepGRAI (Deep Grey Rating via Artificial Intelligence) Registry is a multicentre (30 USA sites), longitudinal, observational, retrospective, real-word study of relapsing-remitting (RR) MS patients. Each centre enrolled between 30 and 35 patients. Brain MRI exams acquired at baseline and follow-up on 1.5T or 3T scanners with no prior standardisation were collected. TV measurement was performed on T2-FLAIR using DeepGRAI, and on two dimensional (D)-weighted and 3D T1-weighted images (WI) by using FMRIB's Integrated Registration and Segmentation Tool software where possible.
RESULTS
1002 RRMS patients were followed for an average of 2.6 years. Longitudinal TV analysis was more readily available on T2-FLAIR (96.1%), compared with 2D-T1-WI (61.8%) or 3D-T1-WI (33.2%). Over the follow-up, DeepGRAI TV loss was significantly higher in patients with DP, compared with those with disability improvement (DI) or disease stability (-1.35% in DP, -0.87% in DI and -0.57% in Stable, p=0.045, Bonferroni-adjusted, age-adjusted and follow-up time-adjusted analysis of covariance). In a regression model including MRI scanner change, age, sex, disease duration and follow-up time, DP was associated with DeepGRAI TV loss (p=0.022).
CONCLUSIONS
Thalamic atrophy measured by AI in a multicentre clinical routine real-word setting is associated with DP over mid-term follow-up.
背景
丘脑是关键的灰质结构，也是多发性硬化症（MS）神经退行性变的敏感标志物。先前的报告表明，仅在临床质量的T2液体衰减反转恢复（FLAIR）图像上使用人工智能（AI）进行丘脑容积测量快速且可靠。
目的
在一项大型多中心研究中，调查通过AI纵向测量的丘脑体积（TV）损失是否与MS患者的残疾进展（DP）相关。
方法
深度灰质评分人工智能（DeepGRAI）注册研究是一项针对复发缓解型（RR）MS患者的多中心（美国30个地点）、纵向、观察性、回顾性、真实世界研究。每个中心招募30至35名患者。收集在1.5T或3T扫描仪上进行的基线和随访时的脑部MRI检查，之前未进行标准化。使用DeepGRAI在T2-FLAIR上进行TV测量，并尽可能使用FMRIB的综合配准和分割工具软件在二维（D）加权和三维（3D）T1加权图像（WI）上进行测量。
结果
1002例RRMS患者平均随访2.6年。与二维T1-WI（61.8%）或三维T1-WI（33.2%）相比，在T2-FLAIR上更容易获得纵向TV分析结果（96.1%）。在随访期间，与残疾改善（DI）或疾病稳定的患者相比，DP患者的DeepGRAI TV损失显著更高（DP组为-1.35%，DI组为-0.87%，稳定组为-0.57%，p=0.045，经Bonferroni校正、年龄校正和随访时间校正的协方差分析）。在一个包括MRI扫描仪更换、年龄、性别、病程和随访时间的回归模型中，DP与DeepGRAI TV损失相关（p=0.022）。
结论
在多中心临床常规真实世界环境中，通过AI测量的丘脑萎缩与中期随访中的DP相关。
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