Motivation: Alzheimer's disease (AD) poses a significant challenge due to global aging, characterized by complex neurodegenerative processes. Goal(s): To investigate MRI-based hippocampal radiomics as diagnostic markers for AD and differentiate mild cognitive impairment (MCI) from dementia in AD. Approach: We integrate T1-weighted imaging (T1WI) with machine learning to develop AD diagnostic and differentiation models. Results: The Random Forest model exhibited the highest classification diagnostic performance, with the highest areas under the curve (AUC) of 0.977 for AD vs. HC and 0.908 for AD-MCI vs. AD-D. Decision curves analysis indicated good clinical utility of the models at probability thresholds between 0.2 and 0.8. Impact: The potential of T1WI-based hippocampal radiomics features for diagnosing AD is emphasized in this study. Our findings provide enhanced insights into AD diagnosis, progression monitoring, and potential management guidelines.
Yin et al. (Tue,) studied this question.