Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals

초록

Background Cortical deposition of beta-amyloid (A beta) plaque is one of the main hallmarks of Alzheimer's disease (AD). While A beta positivity has been the main concern so far, predicting whether A beta (-) individuals will convert to A beta (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from A beta (-) to A beta (+) using artificial intelligence. Methods Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially A beta (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE epsilon 4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). Results A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE epsilon 4) of 0.674, Model 2 (age, gender, APOE epsilon 4, global SUVR) of 0.814, and Model 3 (age, gender, APOE epsilon 4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. Conclusion We developed prediction models regarding A beta positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates.

키워드