Tripathi Shailendra Mohan, McNeil Christopher J, Staff Roger T, Murray Alison D, Wischik Claude M, Schelter Bjoern, Waiter Gordan D
Department of Geriatric Mental Health, King George's Medical University, UP, Lucknow India.
Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD UK.
Nucl Med Mol Imaging. 2025 Jun;59(3):201-208. doi: 10.1007/s13139-025-00908-2. Epub 2025 Feb 24.
Alzheimer's disease (AD) often coexists with other brain pathologies, and we aimed to classify people with AD using 18 F- Flouro-Deoxy-Glucose-Positron Emission Tomography (FDG-PET).
Baseline FDG-PET data were collected as part of two large scale Phase III clinical trials of a novel tau aggregation inhibitor drug, Leuco-Methylthioninium (LMTX®). A total of 794, well-characterised probable AD subjects were included in the study and the images were classified into "typical AD"(temporoparietal hypometabolism) and "mixed" (patchy hypo-metabolism in other vascular territories of brain such as frontal and cerebellar regions along with temporo-parietal hypo-metabolism) patterns based on visual interpretation. The differences in the two groups were further assessed with region-of-interest based analysis of Standardized Uptake Value Ratio (SUVR) and automated classification using transfer learning with visual classification as the gold standard.
Of the total of 794 (438 female) participants, 533 (284 female) were classified as typical AD and 261 (154 female) participants classified as mixed. A subset of 50 images each from typical and mixed subtypes were used for transfer learning and sensitivity, specificity and accuracy for one of the cross-validation loops was 94.73%, 95.23% and 95% respectively. The average accuracy to distinguish the two subtypes after 5-fold cross validation was found to be 97.5%.
This study is first of its kind to distinguish two subtypes of AD through visual interpretation of FDG-PET images and exploring the findings with a semi-quantitative method followed by transfer learning, which has been used to predict the two subtypes with high accuracy, sensitivity and specificity.
阿尔茨海默病（AD）常与其他脑部病变共存，我们旨在利用18F-氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）对AD患者进行分类。
作为新型tau聚集抑制剂药物白甲基硫堇（LMTX®）两项大规模III期临床试验的一部分，收集了基线FDG-PET数据。该研究共纳入794例特征明确的可能AD患者，并根据视觉判读将图像分为“典型AD”（颞顶叶代谢减低）和“混合型”（大脑其他血管区域如额叶和小脑区域出现斑片状代谢减低，同时伴有颞顶叶代谢减低）模式。通过基于感兴趣区域的标准化摄取值比率（SUVR）分析以及以视觉分类为金标准的迁移学习自动分类，进一步评估两组之间的差异。
在总共794名（438名女性）参与者中，533名（284名女性）被分类为典型AD，261名（154名女性）参与者被分类为混合型。从典型和混合型亚型中各选取50幅图像组成一个子集用于迁移学习，其中一个交叉验证循环的灵敏度、特异度和准确度分别为94.73%、95.23%和95%。5折交叉验证后区分两种亚型的平均准确度为97.5%。
本研究首次通过对FDG-PET图像的视觉判读来区分AD的两种亚型，并采用半定量方法探索研究结果，随后进行迁移学习，该方法已被用于以高精度、高灵敏度和高特异度预测这两种亚型。
