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脑淀粉样变性的标准化摄取值比率独立评估

Standardized Uptake Value Ratio-Independent Evaluation of Brain Amyloidosis.

作者信息

Chincarini Andrea, Sensi Francesco, Rei Luca, Bossert Irene, Morbelli Silvia, Guerra Ugo Paolo, Frisoni Giovanni, Padovani Alessandro, Nobili Flavio

机构信息

Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy.

Dipartimento di Fisica, Università degli Studi di Genova, Genova, Italy.

出版信息

J Alzheimers Dis. 2016 Oct 18;54(4):1437-1457. doi: 10.3233/JAD-160232.

Abstract

The assessment of in vivo18F images targeting amyloid deposition is currently carried on by visual rating with an optional quantification based on standardized uptake value ratio (SUVr) measurements. We target the difficulties of image reading and possible shortcomings of the SUVr methods by validating a new semi-quantitative approach named ELBA. ELBA involves a minimal image preprocessing and does not rely on small, specific regions of interest (ROIs). It evaluates the whole brain and delivers a geometrical/intensity score to be used for ranking and dichotomic assessment. The method was applied to adniimages 18F-florbetapir images from the ADNI database. Five expert readers provided visual assessment in blind and open sessions. The longitudinal trend and the comparison to SUVr measurements were also evaluated. ELBA performed with area under the roc curve (AUC) = 0.997 versus the visual assessment. The score was significantly correlated to the SUVr values (r = 0.86, p < 10-4). The longitudinal analysis estimated a test/retest error of ≃2.3%. Cohort and longitudinal analysis suggests that the ELBA method accurately ranks the brain amyloid burden. The expert readers confirmed its relevance in aiding the visual assessment in a significant number (85) of difficult cases. Despite the good performance, poor and uneven image quality constitutes the major limitation.

摘要

目前,针对淀粉样蛋白沉积的体内18F图像评估是通过视觉评分进行的,并可选择基于标准化摄取值比率(SUVr)测量进行定量分析。我们通过验证一种名为ELBA的新半定量方法,来解决图像解读的困难以及SUVr方法可能存在的缺点。ELBA只涉及最少的图像预处理,且不依赖于小的特定感兴趣区域(ROI)。它对全脑进行评估,并给出一个几何/强度分数,用于排序和二分评估。该方法应用于来自阿尔茨海默病神经成像计划(ADNI)数据库的18F-氟比他派图像。五名专家读者在盲法和开放环节中提供了视觉评估。还评估了纵向趋势以及与SUVr测量值的比较。与视觉评估相比,ELBA的曲线下面积(AUC)为0.997。该分数与SUVr值显著相关(r = 0.86,p < 10-4)。纵向分析估计重测误差约为2.3%。队列和纵向分析表明,ELBA方法能够准确地对脑淀粉样蛋白负荷进行排序。专家读者证实,在大量(85例)困难病例中,它有助于视觉评估。尽管性能良好,但图像质量差和不均匀是主要限制因素。

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