Comparison of Radiomics and conventional SUVr methods for Alzheimer's disease classification using AV45 PET imaging.

Objective: To compare the diagnostic performance of radiomics-based analysis and the conventional standardized uptake value ratio (SUVr) method in classifying Alzheimer's disease (AD) and non-Alzheimer's disease (NAD) using AV45 PET imaging. Methods: This retrospective study included 79 patients diagnosed with AD and 34 patients diagnosed with NAD between July 2023 and August 2024. All patients underwent AV45 PET imaging, and the images were registered to a standard template for the extraction of SUVr metrics, including SUVmaxr, SUVmeanr, and SUVmoder, as well as radiomic features (a total of 660 features) from regions of interest (ROIs) in the brain lobes. Feature importance was ranked using a random forest algorithm, and three models were constructed: an SUVr model, a radiomics model, and a combined model. The classification performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Model accuracy, sensitivity, specificity, and precision were evaluated using the Mann-Whitney test, DeLong test, and confusion matrices. Results: There were no significant differences in gender and age between AD and NAD groups ( Conclusion: The radiomics-based approach significantly outperformed the conventional SUVr method, particularly in terms of sensitivity and specificity. This study highlights the potential of radiomics for quantitative PET imaging analysis and its promising clinical applications.