Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

BACKGROUND: The Disease State Index (DSI) prediction model measures the similarity of patient data to diagnosed stable and progressive mild cognitive impairment (MCI) cases to identify patients who are progressing to Alzheimer's disease. OBJECTIVES: We evaluated how well the DSI generalizes across four different cohorts: DESCRIPA, ADNI, AddNeuroMed, and the Kuopio MCI study. METHODS: The accuracy of the DSI in predicting progression was examined for each cohort separately using 10 × 10-fold cross-validation and for inter-cohort validation using each cohort as a test set for the model built from the other independent cohorts using bootstrapping with 10 repetitions. Altogether 875 subjects were included in the analysis. The analyzed data included a comprehensive set of age and gender corrected magnetic resonance imaging (MRI) features from hippocampal volumetry, multi-template tensor-based morphometry, and voxel-based morphometry as well as Mini-Mental State Examination (MMSE), APOE genotype, and additional cohort specific data from neuropsychological tests and cerebrospinal fluid measurements (CSF). RESULTS: The DSI model was used to classify the patients into stable and progressive MCI cases. AddNeuroMed had the highest classification results of the cohorts, while ADNI and Kuopio MCI exhibited the lowest values. The MRI features alone achieved a good classification performance for all cohorts. For ADNI and DESCRIPA, adding MMSE, APOE genotype, CSF, and neuropsychological data improved the results. CONCLUSIONS: The results reveal that the prediction performance of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, if they are sufficiently similar.

Original publication

DOI

10.3233/JAD-140942

Type

Journal article

Journal

J Alzheimers Dis

Publication Date

2015

Volume

44

Pages

79 - 92

Keywords

Alzheimer's disease, computer-assisted diagnosis, dementia, magnetic resonance imaging (MRI), mild cognitive impairment, Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Area Under Curve, Brain, Cognitive Dysfunction, Cohort Studies, Disease Progression, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Mental Status Schedule, Middle Aged, Multicenter Studies as Topic, Neuropsychological Tests, Predictive Value of Tests