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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\n                    <jats:italic>training dataset<\/jats:italic>\n                    from Alzheimer\u2019s 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 \u22654 points. This MRICloud prediction model was then applied to a\n                    <jats:italic>test dataset<\/jats:italic>\n                    of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer\u2019s 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.\n                  <\/jats:p>","DOI":"10.1155\/2019\/9507193","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T18:51:39Z","timestamp":1548787899000},"page":"1-10","source":"Crossref","is-referenced-by-count":14,"title":["Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1310-9403","authenticated-orcid":true,"given":"Ryo","family":"Sakamoto","sequence":"first","affiliation":[{"name":"Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"Department of Radiology, Kyoto University School of Medicine, Kyoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Marano","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview and Johns Hopkins University, Baltimore, MD, USA"},{"name":"Division of Geriatric Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael I.","family":"Miller","sequence":"additional","affiliation":[{"name":"Center for Imaging Science, School or Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"name":"AnatomyWorks, LLC, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3827-4720","authenticated-orcid":true,"given":"Constantine G.","family":"Lyketsos","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview and Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4367-2539","authenticated-orcid":true,"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"AnatomyWorks, LLC, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susumu","family":"Mori","sequence":"additional","affiliation":[{"name":"Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"AnatomyWorks, LLC, Baltimore, MD, USA"},{"name":"F. 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