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Research
Alexander Yuk Lun Lau
Chinese University of Hong Kong
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5933-9290
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: We aimed to validate the performance of an MRI-based machine learning derived Alzheimer’s Disease-resemblance atrophy index (AD-RAI) in detecting preclinical and prodromal AD.
Methods: A total of 62 subjects (mild cognitive impairment [MCI]=25, cognitively unimpaired [CU]=37) underwent MRI, 11C- PIB, and 18F-T807 PET. We investigated the performance of AD-RAI at the pre-specified cutoff of ≥ 0.5 in detecting preclinical and prodromal AD and compared its performance with that of visual and volumetric hippocampal measures.
Results: AD-RAI achieved the best metrics among all subjects (sensitivity 0.73, specificity 0.91, accuracy 87.10%) and among MCI subgroup (sensitivity 0.91, specificity 0.79, accuracy 84.00%) in detecting A+T+ subjects over other measures. Among CU subgroup, hippocampal volume (sensitivity 0.75, specificity 0.88, accuracy 86.49%) achieved a higher sensitivity than AD-RAI (sensitivity 0.25, specificity 0.97, accuracy 89.19%) in detecting preclinical AD.
Conclusions: AD-RAI aids the detection of early AD, in particular at the prodromal stage.
Keywords
preclinical Alzheimer’s Disease, prodromal Alzheimer’s Disease, volumetric segmentation tool, MRI, amyloid PET, tau PET
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Alexander Yuk Lun Lau
Chinese University of Hong Kong
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5933-9290
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Aging.
Version 1
Posted 12 Aug, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cognitive Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Aging.
Background: We aimed to validate the performance of an MRI-based machine learning derived Alzheimer’s Disease-resemblance atrophy index (AD-RAI) in detecting preclinical and prodromal AD.
Methods: A total of 62 subjects (mild cognitive impairment [MCI]=25, cognitively unimpaired [CU]=37) underwent MRI, 11C- PIB, and 18F-T807 PET. We investigated the performance of AD-RAI at the pre-specified cutoff of ≥ 0.5 in detecting preclinical and prodromal AD and compared its performance with that of visual and volumetric hippocampal measures.
Results: AD-RAI achieved the best metrics among all subjects (sensitivity 0.73, specificity 0.91, accuracy 87.10%) and among MCI subgroup (sensitivity 0.91, specificity 0.79, accuracy 84.00%) in detecting A+T+ subjects over other measures. Among CU subgroup, hippocampal volume (sensitivity 0.75, specificity 0.88, accuracy 86.49%) achieved a higher sensitivity than AD-RAI (sensitivity 0.25, specificity 0.97, accuracy 89.19%) in detecting preclinical AD.
Conclusions: AD-RAI aids the detection of early AD, in particular at the prodromal stage.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
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