Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (11:52:28 CEST)
How to cite:
Rasi, R.; Guvenis, A. Radiomics Features of FDG PET Images Predict ApoE4 and may Provide Clues for Further Studies on Alzheimer’s Disease. Preprints2024, 2024092004. https://doi.org/10.20944/preprints202409.2004.v1
Rasi, R.; Guvenis, A. Radiomics Features of FDG PET Images Predict ApoE4 and may Provide Clues for Further Studies on Alzheimer’s Disease. Preprints 2024, 2024092004. https://doi.org/10.20944/preprints202409.2004.v1
Rasi, R.; Guvenis, A. Radiomics Features of FDG PET Images Predict ApoE4 and may Provide Clues for Further Studies on Alzheimer’s Disease. Preprints2024, 2024092004. https://doi.org/10.20944/preprints202409.2004.v1
APA Style
Rasi, R., & Guvenis, A. (2024). Radiomics Features of FDG PET Images Predict ApoE4 and may Provide Clues for Further Studies on Alzheimer’s Disease. Preprints. https://doi.org/10.20944/preprints202409.2004.v1
Chicago/Turabian Style
Rasi, R. and Albert Guvenis. 2024 "Radiomics Features of FDG PET Images Predict ApoE4 and may Provide Clues for Further Studies on Alzheimer’s Disease" Preprints. https://doi.org/10.20944/preprints202409.2004.v1
Abstract
Objective: Fludeoxyglucose F18 positron emission tomography (FDG PET) can give early clues for diagnosing Alzheimer’s disease (AD). Our objective was to use the same scan to predict Apolipoprotein E4 (ApoE4), known to be a risk factor. A second objective was to determine the brain regions and imaging features associated with this gene allele that can potentially help elucidate the mechanisms of the disease and better comprehend the images. Method: We employed data from 112 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). PET and structural MRI data were co-registered with ANTx, and the Freesurfer tool delineated 95 whole brain, 19 hippocampal, and 9 amygdala regions. Using PyRadiomics we extracted 120 radiomic features from these segments. We employed a hybrid feature selection strategy. The HistGradientBoosting (HGB) classifier was evaluated using stratified five-fold cross-validation. Results: The proposed radiomics model predicted the homozygous ApoE4 genotype, with an AUC of 0.945 an accuracy of 0.875, a sensitivity of 0.914, and a specificity of 0.838. Reducing the feature set to seven key features resulted in a slightly decreased performance (AUC: 0.889). The mean values of the features showed statistically significant (p<0.05) incremental deterioration between different groups of homozygous ApoE4 carriers. Conclusion: Our findings underscore the critical role of specific brain regions and associated features in differentiating ApoE4 carriers. The hippocampus, entorhinal cortex, amygdala, thalamus, and pars orbitalis exhibited significant associations with the ApoE4 genotype. By employing SHAP analysis, we identified key features driving model predictions, enhancing interpretability, and providing insights into the underlying pathophysiology.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
