Stoel Berend C, Marquering Henk A, Staring Marius, Beenen Ludo F, Slump Cornelis H, Roos Yvo B, Majoie Charles B
Leiden University Medical Center , Division of Image Processing, Department of Radiology, Albinusdreef 2 Leiden 2333 AA, The Netherlands.
Academic Medical Center , Department of Radiology, Amsterdam, The Netherlands ; Academic Medical Center , Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands.
J Med Imaging (Bellingham). 2015 Jan;2(1):014004. doi: 10.1117/1.JMI.2.1.014004. Epub 2015 Mar 24.
The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS ([Formula: see text] and [Formula: see text], respectively) and with follow-up mRS ([Formula: see text] and [Formula: see text]), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences [Formula: see text] point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.
艾伯塔卒中项目早期CT评分(ASPECTS)评分方法在临床研究中常用于量化急性缺血性卒中患者的早期缺血性改变(EICs)。然而,观察者间的一致性存在差异,且一致性有限。因此,我们的目标是开发并评估一种自动脑密度测量方法。该方法使用基于图谱的分割将脑部CT扫描分为ASPECTS区域。通过比较对侧脑密度来量化EICs。分别使用10例和63例患者的CT数据对该方法进行了优化和验证。将自动方法与手动ASPECTS、基线时的卒中严重程度以及7至10天(美国国立卫生研究院卒中量表,NIHSS)和3个月(改良Rankin量表)后的临床结局进行了验证。手动和自动ASPECTS与基线NIHSS(分别为[公式:见原文]和[公式:见原文])以及随访mRS(分别为[公式:见原文]和[公式:见原文])显示出相似且具有统计学意义的相关性,但随访NIHSS除外。自动与共识ASPECTS读数之间的一致性与手动ASPECTS的观察者间一致性相似(73%的病例差异[公式:见原文]分)。因此,自动ASPECTS方法可作为辅助手动评分的补充工具。