Radiomics Analysis of Subcortical Brain Regions Related to Alzheimer Disease

Abstract

Alzheimer's disease (AD) is the most common form of dementia that causes progressive impairment of memory and cognitive functions of patients. However, whether imaging features can be utilised as biomarkers for this disease has not been explored. To address this, we encoded subcortical regions of the brain using 45 radiomic features to identify features specific for AD patients. We comprehensively evaluated the proposed approach using the OASIS dataset, assessing significance via the Wilcoxon test and Random Forest (RF) classifier models to identify the subcortical regions best able to identify AD patients. Our results show that features (i.e., correlation and volume) derived from several subcortical regions (i.e., cerebral, thalamus, caudate Putamen, Pallidum, hippocampus, amygdala, and stem-and-cerebrospinal-fluid) are able to identify AD from healthy control (HC) subjects with the hippocampus and amygdala reaching $\mathrm{p} < 0.01$ following Holm-Bonferroni correction. Consistent with this, hippocampus ($\mathbf{AUC}=\mathbf{81.19-84.09}\%$) and amygdala ($\mathbf{AUC}=\mathbf{79.70-80.27}\%$) regions showed a higher AUC value compared to other subcortical regions. Combining radiomic features derived from all subcortical regions produced an AUC value of 91.54% for classifying AD from HC subjects. RF analysis revealed that from the 45 radiomic features, correlation and volume are the most important features for the classifier model. These results demonstrate that radiomic features extracted from hippocampus and amygdala regions are relevant biomarkers for AD patients and that correlation and volume features are the most important features to build this model.