Scientific article
Open access

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease

Published inFrontiers in big data, vol. 4, 661110
Publication date2021
First online date2021-05-20

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

  • SuStain model
  • Alzheiemer’s disease
  • Inter-cohort validation
  • Patient staging
  • Patient subtyping
  • European Commission - Data-driven models for Progression Of Neurological Disease [666992]
  • European Commission - A Clinical Decision Support system based on Quantitative multimodal brain MRI for personalized treatment in neurological and psychiatric disorders [634541]
  • National Institutes of Health - [U01 AG024904]
  • US Department of Defense - Alzheimer’s Disease Neuroimaging Initiative [W81XWH-12-2-0012]
Citation (ISO format)
ARCHETTI, Damiano et al. Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease. In: Frontiers in big data, 2021, vol. 4, p. 661110. doi: 10.3389/fdata.2021.661110
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ISSN of the journal2624-909X

Technical informations

Creation11/16/2022 11:25:15 AM
First validation07/04/2023 3:51:11 PM
Update time07/04/2023 3:51:11 PM
Status update07/04/2023 3:51:11 PM
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