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多中心验证在多发性硬化症中通过脑磁共振扫描自动检测磁边病变

Luyun Chen^1,2, Zheng Ren¹, Kelly A Clark¹

¹Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Journal of neuroimaging : official journal of the American Society of Neuroimaging

|October 16, 2024

概括

自动化偏磁边缘损伤 (APRL) 算法在多发性硬化症 (MS) 患者中准确检测偏磁边缘损伤 (PRL). 这种自动化方法提高了大规模临床试验评估的效率.

科学领域:

神经学神经学
放射学放射学是一门学科.
生物标志物发现发现

背景情况:

偏磁边缘病变 (PRL) 是多发性硬化症 (MS) 慢性炎症的关键MRI生物标志物.
手动识别PRL是劳动密集型的,缺乏评分器之间的可靠性.
需要自动检测PRLs以有效诊断和预后.

研究的目的:

评估自动化偏磁边缘损伤 (APRL) 算法用于检测PRLs的准确性.
评估APRL在多中心诊断多发性硬化症 (MS) 的性能.

主要方法:

该APRL算法应用于92名参与者的多中心3-特斯拉MRI数据集.
将APRL的性能与受过训练的评级人员手动进行的PRL评估进行了比较.
统计分析包括病变和主体级预测曲线下的面积 (AUC).

主要成果:

在手工检测的PRL中,APRL成功识别了78%的PRL.
该算法将PRL与非PRL区分开来,其AUC为0.73.
在APRL中识别的PRL计数预测MS诊断,AUC为0.69.

结论:

在多中心环境中,APRL算法准确地将PRL与其他病变区分开来.
与手动方法相比,自动化PRL识别提供了更高的效率.
在MS的临床试验中,APRL可以促进大规模的评估.

关键词:

自动化检测检测自动化检测多发性硬化症多发性硬化症偏磁边缘损伤损伤偏磁边缘损伤

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