ArticleClinical nuclear medicine2025
A Robust Residual Three-dimensional Convolutional Neural Networks Model for Prediction of Amyloid-β Positivity by Using FDG-PET.
Article in Clinical nuclear medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.Japanese journal of radiology · 2026Review
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
backgroundWidely used in oncology PET, 2-deoxy-2- 18 F-FDG PET is more accessible and affordable than amyloid PET, which is a crucial tool to determine amyloid positivity in diagnosis of Alzheimer disease (AD). This study aimed to leverage deep learning with residual 3D convolutional neural networks (3DCNN) to develop a robust model that predicts amyloid-β positivity by using FDG-PET. PATIENTS AND
methodsIn this study, a cohort of 187 patients was used for model development. It consisted of patients ranging from cognitively normal to those with dementia and other cognitive impairments who underwent T1-weighted MRI, 18 F-FDG, and 11 C-Pittsburgh compound B (PiB) PET scans. A residual 3DCNN model was configured using nonexhaustive grid search and trained on repeated random splits of our development data set. We evaluated the performance of our model, and particularly its robustness, using a multisite data set of 99 patients of different ethnicities with images at different site harmonization levels.
resultsOur model achieved mean AUC scores of 0.815 and 0.840 on images without and with site harmonization correspondingly. Respectively, it achieved higher AUC scores of 0.801 and 0.834 in the cognitively normal (CN) group compared with 0.777 and 0.745 in the dementia group. As for F1 score, the corresponding mean scores were 0.770 and 0.810 on images without and with site harmonization. In the CN group, it achieved lower F1 scores of 0.580 and 0.658 compared with 0.907 and 0.931 in the dementia group.
conclusionsWe demonstrated that residual 3DCNN can learn complex 3D spatial patterns in FDG-PET images and robustly predict amyloid-β positivity with significantly less reliance on site harmonization preprocessing.
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