Detecting amyloid‐β positivity using regions of interest from structural magnetic resonance imaging

Abstract Background and purpose Alzheimer disease (AD) is the most common type of dementia. Amyloid‐β (Aβ) positivity is the main diagnostic marker for AD. Aβ positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of Aβ, and the accessibility of these methods is thus relatively limited compared with structural magnetic resonance imaging (sMRI). Methods We investigated whether regions of interest (ROIs) in sMRIs can be used to predict Aβ positivity for samples with normal cognition (NC), mild cognitive impairment (MCI), and dementia. We obtained 846 Aβ negative (Aβ−) and 865 Aβ positive (Aβ+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are Aβ+, we built five machine learning models using ROIs and apolipoprotein E ( APOE ) genotypes as features. To test the performance of the machine learning models, we constructed a new cohort containing 97 Aβ− and 81 Aβ+ samples. Results The best performing machine learning model combining ROIs and APOE had an accuracy of 0.798, indicating that it can help predict Aβ+. Furthermore, we searched ROIs that could aid our prediction and discovered that an average left entorhinal cortical region (L‐ERC) thickness is an important feature. We also noted significant differences in L‐ERC thickness between the Aβ− and Aβ+ samples even in the same diagnosis of NC, MCI, and dementia. Conclusions Our findings indicate that ROIs from sMRIs along with APOE can be used as an initial screening tool in the early diagnosis of AD.