Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening
Joint Authors
Neuroimaging Initiative, The Alzheimer’s Disease
Sakamoto, Ryo
Marano, Christopher
Lyketsos, Constantine G.
Li, Yue
Mori, Susumu
Oishi, Kenichi
Miller, Michael I.
Source
Journal of Healthcare Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers.
Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making.
To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud.
The model was built in a training dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time.
Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel.
The Mini Mental State Examination (MMSE) was used as a measure of cognitive function.
The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction.
A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change.
A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points.
This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer’s Treatment Center (MATC), a clinical care setting.
In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening.
While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.
American Psychological Association (APA)
Sakamoto, Ryo& Marano, Christopher& Miller, Michael I.& Lyketsos, Constantine G.& Li, Yue& Mori, Susumu…[et al.]. 2019. Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175472
Modern Language Association (MLA)
Sakamoto, Ryo…[et al.]. Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening. Journal of Healthcare Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1175472
American Medical Association (AMA)
Sakamoto, Ryo& Marano, Christopher& Miller, Michael I.& Lyketsos, Constantine G.& Li, Yue& Mori, Susumu…[et al.]. Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1175472
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175472