Multi-scale graph-based grading for Alzheimer’s disease prediction - Hett, Kilian; Ta, Vinh-Thong; Oguz, Ipek; Manjón, José V.; Coupé, Pierrick (Medical image analysis, 2021)
•This work introduces a new approach combining patch-based grading and graph-based model.•Combination of inter-subject similarity and intra-subject variability helps to predict Alzheimer’s disease.•Analysis of whole brain structures and hippocampal subfields jointly enables improvement of prediction performances.•Image-based biomarkers and cognitive data are highly complementary for Alzheimer’s disease prediction. [Display omitted] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.