Alzheimer's research & therapy · 2022
Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals.
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Park Chae Jung, Seo Younghoon, Choe Yeong Sim, Jang Hyemin, Lee Hyejoo, Kim Jun Pyo, Alzheimer’s Disease Neuroimaging Initiative
Аннотация
BACKGROUND: Cortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer's disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (-) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (-) to Aβ (+) using artificial intelligence.
METHODS: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 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 ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900.
CONCLUSION: We developed prediction models regarding Aβ 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.
METHODS: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 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 ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900.
CONCLUSION: We developed prediction models regarding Aβ 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.