Ian P Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D'Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S Rosen, Annabel Sorby-Adams, W Taylor Kimberly, Kevin N Sheth, Adam de Havenon
{"title":"Predicting White Matter Hyperintensity: Leveraging Portable Magnetic Resonance Imaging for Accessible Brain Health Screening.","authors":"Ian P Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D'Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S Rosen, Annabel Sorby-Adams, W Taylor Kimberly, Kevin N Sheth, Adam de Havenon","doi":"10.3174/ajnr.A8734","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>Portable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening.</p><p><strong>Materials and methods: </strong>The development (N=143) and validation (N=127) cohorts included patients without acute neurologic pathology who received a pMRI at a tertiary care hospital between May 2020 and July 2024. The development cohort included pMRIs collected as part of a prospective WMH screening pilot program in the emergency department. The validation cohort was a retrospective collection of pMRIs obtained for separate research purposes. Conventional MRIs (cMRIs) in the validation cohort obtained within 3 months of pMRIs were used for additional validation and device agreement. The primary outcome was WMH burden greater than 10 mL, assessed via an axial T2-FLAIR sequence acquired on a 0.064 T pMRI and quantified using a WMH segmentation software developed to process sequences of any resolution. We used backwards selection to screen candidate variables and report the area under the curve of the resulting model.</p><p><strong>Results: </strong>The final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an AUC of 0.83 (95% CI 0.75-0.90) in the development cohort (N=143, 62.4±12.6 years, 44% female, 36% non-white race) and 0.85 (95% CI 0.77-0.92) in the validation cohort (N=127, 65.2±16.8 years, 51% female, 34% non-white race), with similar results using WMH measurements derived from cMRI (N=120, p=0.98, AUC=0.86, 95% CI 0.77-0.93). Additionally, we confirmed agreement in WMH volumes between pMRI and cMRI (N=120, r=0.93, 95% CI 0.90-0.95, p<0.001).</p><p><strong>Conclusions: </strong>The WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of significant WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurological outcomes.</p><p><strong>Abbreviations: </strong>pMRI = portable magnetic resonance imaging; cMRI = conventional magnetic resonance imaging; WMH = white matter hyperintensity; hypertension = HTN; diabetes = DM; atrial fibrillation = AFib; systolic blood pressure = SBP; hyperlipidemia = HLD; area under the curve = AUC; receiver operating characteristic = ROC.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AJNR. American journal of neuroradiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3174/ajnr.A8734","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background and purpose: Portable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening.
Materials and methods: The development (N=143) and validation (N=127) cohorts included patients without acute neurologic pathology who received a pMRI at a tertiary care hospital between May 2020 and July 2024. The development cohort included pMRIs collected as part of a prospective WMH screening pilot program in the emergency department. The validation cohort was a retrospective collection of pMRIs obtained for separate research purposes. Conventional MRIs (cMRIs) in the validation cohort obtained within 3 months of pMRIs were used for additional validation and device agreement. The primary outcome was WMH burden greater than 10 mL, assessed via an axial T2-FLAIR sequence acquired on a 0.064 T pMRI and quantified using a WMH segmentation software developed to process sequences of any resolution. We used backwards selection to screen candidate variables and report the area under the curve of the resulting model.
Results: The final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an AUC of 0.83 (95% CI 0.75-0.90) in the development cohort (N=143, 62.4±12.6 years, 44% female, 36% non-white race) and 0.85 (95% CI 0.77-0.92) in the validation cohort (N=127, 65.2±16.8 years, 51% female, 34% non-white race), with similar results using WMH measurements derived from cMRI (N=120, p=0.98, AUC=0.86, 95% CI 0.77-0.93). Additionally, we confirmed agreement in WMH volumes between pMRI and cMRI (N=120, r=0.93, 95% CI 0.90-0.95, p<0.001).
Conclusions: The WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of significant WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurological outcomes.
Abbreviations: pMRI = portable magnetic resonance imaging; cMRI = conventional magnetic resonance imaging; WMH = white matter hyperintensity; hypertension = HTN; diabetes = DM; atrial fibrillation = AFib; systolic blood pressure = SBP; hyperlipidemia = HLD; area under the curve = AUC; receiver operating characteristic = ROC.