Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening
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 predicti...
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doaj-c8e20a3693a849daa6334aeeb303c1fd2020-11-25T00:58:13ZengHindawi LimitedJournal of Healthcare Engineering2040-22952040-23092019-01-01201910.1155/2019/95071939507193Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive WorseningRyo Sakamoto0Christopher Marano1Michael I. Miller2Constantine G. Lyketsos3Yue Li4Susumu Mori5Kenichi Oishi6Alzheimer’s Disease Neuroimaging Initiative ADNI7Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartment of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview and Johns Hopkins University, Baltimore, MD, USACenter for Imaging Science, School or Engineering, Johns Hopkins University, Baltimore, MD, USADepartment of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview and Johns Hopkins University, Baltimore, MD, USAAnatomyWorks, LLC, Baltimore, MD, USADepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartment of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USAAlzheimer’s Disease Neuroimaging Initiative Study, USAFor 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.http://dx.doi.org/10.1155/2019/9507193 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ryo Sakamoto Christopher Marano Michael I. Miller Constantine G. Lyketsos Yue Li Susumu Mori Kenichi Oishi Alzheimer’s Disease Neuroimaging Initiative ADNI |
spellingShingle |
Ryo Sakamoto Christopher Marano Michael I. Miller Constantine G. Lyketsos Yue Li Susumu Mori Kenichi Oishi Alzheimer’s Disease Neuroimaging Initiative ADNI Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening Journal of Healthcare Engineering |
author_facet |
Ryo Sakamoto Christopher Marano Michael I. Miller Constantine G. Lyketsos Yue Li Susumu Mori Kenichi Oishi Alzheimer’s Disease Neuroimaging Initiative ADNI |
author_sort |
Ryo Sakamoto |
title |
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
title_short |
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
title_full |
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
title_fullStr |
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
title_full_unstemmed |
Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
title_sort |
cloud-based brain magnetic resonance image segmentation and parcellation system for individualized prediction of cognitive worsening |
publisher |
Hindawi Limited |
series |
Journal of Healthcare Engineering |
issn |
2040-2295 2040-2309 |
publishDate |
2019-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2019/9507193 |
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