Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume

• Published in: Journal of psychiatry & neuroscience 2020-01, Vol.45 (1), p.7-14
• Main Authors: Lee, Subin, Lee, Hyunna, Kim, Ki Woong
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Atrophy; Automation; Brain; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Cortex (cingulate); Cortex (parietal); Datasets; Dementia; Dementia disorders; Diagnostic imaging; Disease Progression; Female; Gyrus Cinguli - diagnostic imaging; Gyrus Cinguli - pathology; Hippocampus; Hippocampus - diagnostic imaging; Hippocampus - pathology; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Medical imaging; Medical research; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Neuroimaging - standards; Parietal Lobe - diagnostic imaging; Parietal Lobe - pathology; Pharmaceutical industry; Predictions; Prognosis; Reproducibility of Results; Research Paper; Studies
• ISSN: 1180-4882; 1488-2434
• DOI: 10.1503/jpn.180171

Early identification of people at risk of imminent progression to dementia due to Alzheimer disease is crucial for timely intervention and treatment. We investigated whether the texture of MRI brain scans could predict the progression of mild cognitive impairment (MCI) to Alzheimer disease earlier than volume. We constructed a development data set (121 people who were cognitively normal and 145 who had mild Alzheimer disease) and a validation data set (113 patients with stable MCI who did not progress to Alzheimer disease for 3 years; 40 with early MCI who progressed to Alzheimer disease after 12–36 months; and 41 with late MCI who progressed to Alzheimer disease within 12 months) from the Alzheimer’s Disease Neuroimaging Initiative. We analyzed the texture of the hippocampus, precuneus and posterior cingulate cortex using a grey-level co-occurrence matrix. We constructed texture and volume indices from the development data set using logistic regression. Using area under the curve (AUC) of receiver operator characteristics, we compared the accuracy of hippocampal volume, hippocampal texture and the composite texture of the hippocampus, precuneus and posterior cingulate cortex in predicting conversion from MCI to Alzheimer disease in the validation data set. Compared with hippocampal volume, hippocampal texture (0.790 v. 0.739, p = 0.047) and composite texture (0.811 v. 0.739, p = 0.007) showed larger AUCs for conversion to Alzheimer disease from both early and late MCI. Hippocampal texture showed a marginally larger AUC than hippocampal volume in early MCI (0.795 v. 0.726, p = 0.060). Composite texture showed a larger AUC for conversion to Alzheimer disease than hippocampal volume in both early (0.817 v. 0.726, p = 0.027) and late MCI (0.805 v. 0.753, p = 0.019). This study was limited by the absence of histological data, and the pathology reflected by the texture measures remains to be validated. Textures of the hippocampus, precuneus and posterior cingulate cortex predicted conversion from MCI to Alzheimer disease at an earlier time point and with higher accuracy than hippocampal volume.