NeuroImage · 2025
MR-guided graph learning of 18F-florbetapir PET enables accurate and interpretable Alzheimer's disease staging.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Xinyi, Chen Lijuan, Yao Weiheng, Zuo Qiankun, Li Ye, Liang Dong, Wang Shuqiang, Wang Meiyun, Sun Tao
Аннотация
PURPOSE: Subtle structural and molecular brain changes make noninvasive early detection and staging of Alzheimer's disease (AD) challenging and critical for effective intervention. This study develops a novel graph convolutional network (GCN) learning framework that integrates amyloid-β PET imaging and MRI structural features, aiming for improved early detection and accurate staging of AD.
METHODS: The retrospective study utilized 18F-florbetapir PET scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) as the training dataset (323 scans from 196 subjects - 45 normal control, 80 mild cognitive impairment/MCI, 71 AD) and two independent datasets for testing (99 scans from 85 subjects - 31 normal control, 15 MCI, 44 AD). Individual brain graphs were constructed for each PET scan, and graph learning framework was designed to extract molecular features from PET while integrating structural features from MRI. Performance was evaluated using receiver operating characteristic (ROC) analysis, comparing results against cortical SUVR. Additionally, a biomarker GCN_score was defined based on identified salient regions-of-interest, with its effectiveness assessed using the Kruskal-Wallis test and Cohen's effect size.
RESULTS: The framework achieved AUCs of 89.8 % (specificity 83.6 %, sensitivity 81.6 %) for distinguishing MCI from normal controls and 88.3 % (specificity 81.6 %, sensitivity 80.6 %) for MCI from AD in the ADNI dataset, with comparable performance in external testing. All results significantly outperformed cortical SUVR (DeLong test p < 0.001). The GCN_score demonstrated superior group differentiation (Cohen's effect sizes 1.744 and 1.32) compared to cortical SUVR (0.309 and 0.641).
CONCLUSION: The proposed graph-based learning framework effectively integrates PET and MRI features for accurate AD stage distinction, showing significant promise for early detection and facilitating timely intervention.
METHODS: The retrospective study utilized 18F-florbetapir PET scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) as the training dataset (323 scans from 196 subjects - 45 normal control, 80 mild cognitive impairment/MCI, 71 AD) and two independent datasets for testing (99 scans from 85 subjects - 31 normal control, 15 MCI, 44 AD). Individual brain graphs were constructed for each PET scan, and graph learning framework was designed to extract molecular features from PET while integrating structural features from MRI. Performance was evaluated using receiver operating characteristic (ROC) analysis, comparing results against cortical SUVR. Additionally, a biomarker GCN_score was defined based on identified salient regions-of-interest, with its effectiveness assessed using the Kruskal-Wallis test and Cohen's effect size.
RESULTS: The framework achieved AUCs of 89.8 % (specificity 83.6 %, sensitivity 81.6 %) for distinguishing MCI from normal controls and 88.3 % (specificity 81.6 %, sensitivity 80.6 %) for MCI from AD in the ADNI dataset, with comparable performance in external testing. All results significantly outperformed cortical SUVR (DeLong test p < 0.001). The GCN_score demonstrated superior group differentiation (Cohen's effect sizes 1.744 and 1.32) compared to cortical SUVR (0.309 and 0.641).
CONCLUSION: The proposed graph-based learning framework effectively integrates PET and MRI features for accurate AD stage distinction, showing significant promise for early detection and facilitating timely intervention.