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Objective: This study introduces a PET-based platform for brain radiomics analysis. We automatically identify key brain regions and features associated with Alzheimer's disease (AD), enabling more accurate diagnosis and staging compared to using predefined regions. Methods: To create an integrated platform that covers all the phases of radiomics, we obtained FDG-PET images of 549 individuals from the ADNI database. We used FastSurfer to segment the brain into 95 regions. We then obtained 120 features for each of the 95 ROIs. We employed eight feature selection methods to select and analyze the features. We finally utilized nine different classifiers on the 20 most significant features extracted. Results: For all three predictions AD vs. cognitively normal (CN), AD vs. mild cognitive impairments (MCI), and CN vs. MCI the Random Forest (RF) classifier with LASSO demonstrated the highest accuracy with an AUC of 0.976 for AD vs CN, AUC=0.917 for AD vs MCI, and AUC=0.877 for MCI vs CN. This is the highest performance that we encountered compared to the studies in the literature. Three subregions hippocampus, entorhinal, and amygdala could then be identified as critical. Conclusion: A brain radiomics platform can enable an efficient, standardized, and optimally accurate AD and MCI diagnosis from FDG PET images by using an automated pipeline. The three regions identified as having the highest discriminating power confirm the findings of previous clinical research results on AD. While the focus was AD in this study, the platform can potentially be used to address other brain conditions. |
