Park Su Yeon, Lee Inki, Lim Ilhan, Kim Byung Il, Choi Chang Woon, Ko In Ok, Byun Byung Hyun, Ha Jeong Ho
Departments of Neurology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), 75 Nowongil, Nowon Gu, Seoul, 139-706 Republic of Korea.
Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), 75 Nowongil, Nowon Gu, Seoul, 139-706 Republic of Korea.
Nucl Med Mol Imaging. 2025 Aug;59(4):229-238. doi: 10.1007/s13139-025-00911-7. Epub 2025 Feb 20.
The primary endpoint of this study is to establish a reliable SUVR cutoff threshold to distinguish patients with Alzheimer's disease (AD), excluding those with mild cognitive impairment (MCI), from normal control (NC) individuals using [F]florapronol PET imaging and deep learning-based automated quantification software. The secondary endpoint is to evaluate whether combining partial volume correction (PVC) with SUVR analysis improves diagnostic accuracy in detecting AD.
A total of 141 participants, including 55 AD patients (excluding MCI) and 86 NC controls, were enrolled. Each participant underwent [F]florapronol PET imaging, and SUVR values were calculated for six amyloid-prone brain regions using deep learning-based software. SUVRs were computed with and without PVC, using the cerebellar cortex as the reference region. Receiver operating characteristic (ROC) analysis identified optimal SUVR thresholds for distinguishing AD (excluding MCI) from NC and for determining visual positivity. Age-matched subgroup analyses ensured consistent diagnostic performance across different age groups.
In the full cohort ( = 141), visual analysis achieved a sensitivity of 90.9% and specificity of 94.1% for distinguishing AD from NC. SUVR without PVC reached a similar sensitivity of 90.9% and specificity of 86.0% (optimal threshold > 1.26), while PVC-adjusted SUVR further improved accuracy with a sensitivity of 90.9% and specificity of 94.2% at a threshold of > 1.31. For visual positivity, SUVR without PVC achieved 92.7% sensitivity and 89.5% specificity, while PVC-adjusted SUVR improved these metrics to 96.4% sensitivity and 94.2% specificity. Age-matched analyses confirmed diagnostic consistency across different age groups. The visual analysis and the quantitative analysis using SUVR with PVC as the threshold were consistent in 134 out of 141 subjects (95.0%).
Automated SUVR quantification with PVC adjustment provides a reliable and objective method for distinguishing AD from NC, aligning closely with visual assessment accuracy and supporting clinical use of [F]florapronol PET imaging for AD diagnosis. This standardized approach enhances diagnostic consistency, particularly in settings with limited access to PET specialists, and establishes robust SUVR thresholds for broader clinical application in amyloid PET imaging.
本研究的主要终点是使用[F]氟罗普诺正电子发射断层扫描（PET）成像和基于深度学习的自动定量软件，建立一个可靠的标准化摄取值比率（SUVR）截止阈值，以区分阿尔茨海默病（AD）患者（不包括轻度认知障碍（MCI）患者）与正常对照（NC）个体。次要终点是评估将部分容积校正（PVC）与SUVR分析相结合是否能提高检测AD的诊断准确性。
共招募了141名参与者，包括55名AD患者（不包括MCI）和86名NC对照。每位参与者均接受了[F]氟罗普诺PET成像，并使用基于深度学习的软件计算了六个易发生淀粉样蛋白沉积的脑区的SUVR值。使用小脑皮质作为参考区域，分别计算有和没有PVC时的SUVR。通过受试者操作特征（ROC）分析确定区分AD（不包括MCI）与NC以及确定视觉阳性的最佳SUVR阈值。年龄匹配的亚组分析确保了不同年龄组的诊断性能一致。
在整个队列（n = 141）中，视觉分析区分AD与NC的灵敏度为90.9%，特异性为94.1%。未进行PVC校正的SUVR达到了相似的灵敏度90.9%和特异性86.0%（最佳阈值>1.26），而经PVC校正的SUVR进一步提高了准确性，在阈值>1.31时，灵敏度为90.9%，特异性为94.2%。对于视觉阳性，未进行PVC校正的SUVR灵敏度为92.7%，特异性为89.5%，而经PVC校正的SUVR将这些指标提高到了灵敏度96.4%，特异性94.2%。年龄匹配分析证实了不同年龄组的诊断一致性。在141名受试者中有134名（95.0%）的视觉分析和以经PVC校正的SUVR为阈值的定量分析结果一致。
经PVC校正的自动SUVR定量为区分AD与NC提供了一种可靠且客观的方法，与视觉评估准确性密切相关，并支持[F]氟罗普诺PET成像在AD诊断中的临床应用。这种标准化方法提高了诊断一致性，特别是在PET专家资源有限的情况下，并为淀粉样蛋白PET成像在更广泛的临床应用中建立了可靠的SUVR阈值。
