J Korean Soc Radiol.  2023 May;84(3):638-652. 10.3348/jksr.2022.0084.

Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning

• Park HJ
• Lee JY
• Yang JJ
• Kim HJ
• Kim YS
• Kim JY
• Choi YY

• KMID: 2542897
• DOI: http://doi.org/10.3348/jksr.2022.0084

Abstract

Purpose
To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method.
Materials and Methods
This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity.
Results
The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The vol-umes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes.
Conclusion
The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

Keyword

Amyloid Beta-Peptides; Third Ventricle; Neuroimaging; Support Vector Machine