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多中心研究视觉图像解读与 [F]flutemetamol 淀粉样 PET 图像定量分析的一致性。

A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [F]flutemetamol amyloid PET images.

机构信息

Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.

出版信息

Eur J Nucl Med Mol Imaging. 2021 Jul;48(7):2183-2199. doi: 10.1007/s00259-021-05311-5. Epub 2021 Apr 12.

DOI:10.1007/s00259-021-05311-5
PMID:33844055
原文链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC8175298/
Abstract

BACKGROUND

[F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases.

METHODS

A total of 2770 [F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD).

RESULTS

Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region.

CONCLUSIONS

Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.

摘要

背景

[F]flutemetamol PET 扫描可提供脑淀粉样蛋白负荷信息,已基于视觉解读(阴性表示无或稀疏淀粉样斑块,阳性表示中度或频繁斑块)被批准用于常规临床应用。然而,定量分析对于核医学实践至关重要,因此可以用于补充淀粉样蛋白阅读方法,尤其是在不明确的情况下。

方法

从 3 项临床研究和 6 项具有 [F]flutemetamol 视觉阅读和图像定量分析的研究队列中收集了 2770 份[F]flutemetamol 图像。进一步评估这些图像,主要使用病理学作为金标准建立的稳健阈值来检查视觉和定量成像之间的差异和一致性。这些扫描涵盖了广泛的病例(即从认知正常的受试者到参加记忆诊所的患者)。定量淀粉样蛋白的方法包括使用 CE/510K 清除标记软件(如 CortexID、Brass)和其他基于研究的方法(如 PMOD、CapAIBL)。此外,使用竞争风险回归分析检查了视觉和定量之间的两种类型的差异(V+Q-和 V-Q+)的临床随访,以评估其对阿尔茨海默病(AD)和其他诊断(OD)进展预测的可能差异。

结果

使用尸检衍生阈值,使用脑桥作为参考区域的视觉和定量之间的加权平均一致性为 94%。使用一系列截止值评估每个队列的最大一致性的敏感性分析也估计为约 96%(加权平均)。与研究病例相比,临床病例的一致性通常更高。对于使用脑桥作为参考区域的 SUVr,V-Q+ 不一致病例进展为 AD 的可能性比 V+Q-病例高 11%。

结论

淀粉样蛋白 PET 的定量分析与二元视觉阅读具有高度一致性,并且还允许进行连续测量,该测量可与可能的不一致性分析结合使用,以识别可能更早的病理沉积,并监测疾病进展和治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/d711497a2118/259_2021_5311_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/0de680ff46a9/259_2021_5311_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/b60a5e6c403d/259_2021_5311_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/d711497a2118/259_2021_5311_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/0de680ff46a9/259_2021_5311_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/b60a5e6c403d/259_2021_5311_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/8175298/d711497a2118/259_2021_5311_Fig3_HTML.jpg

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