Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients
Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and tim...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-08-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2018.00687/full |
| _version_ | 1819259809698938880 |
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| author | Rajat Dhar Yasheng Chen Hongyu An Jin-Moo Lee |
| author_facet | Rajat Dhar Yasheng Chen Hongyu An Jin-Moo Lee |
| author_sort | Rajat Dhar |
| collection | DOAJ |
| description | Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (R = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, R = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (R = 0.715) and degree of midline shift (quadratic model, p < 2.2 × 10−16). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (p < 2.2 × 10−13) and is lower in those with cerebral edema (p = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity. |
| first_indexed | 2024-12-23T19:15:55Z |
| format | Article |
| id | doaj.art-c1d9327cbec14b898328bd39b422797b |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-23T19:15:55Z |
| publishDate | 2018-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-c1d9327cbec14b898328bd39b422797b2022-12-21T17:34:20ZengFrontiers Media S.A.Frontiers in Neurology1664-22952018-08-01910.3389/fneur.2018.00687393383Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke PatientsRajat Dhar0Yasheng Chen1Hongyu An2Jin-Moo Lee3Division of Neurocritical Care, Department of Neurology, Washington University in St. Louis, St. Louis, MO, United StatesDivision of Cerebrovascular Diseases, Department of Neurology, Washington University in St. Louis, St. Louis, MO, United StatesDepartment of Radiology, Washington University in St. Louis, St. Louis, MO, United StatesDivision of Cerebrovascular Diseases, Department of Neurology, Washington University in St. Louis, St. Louis, MO, United StatesCerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (R = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, R = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (R = 0.715) and degree of midline shift (quadratic model, p < 2.2 × 10−16). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (p < 2.2 × 10−13) and is lower in those with cerebral edema (p = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity.https://www.frontiersin.org/article/10.3389/fneur.2018.00687/fullischemic strokemachine learningcerebral edemaimage analysis and processingCT scanCSF volume |
| spellingShingle | Rajat Dhar Yasheng Chen Hongyu An Jin-Moo Lee Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients Frontiers in Neurology ischemic stroke machine learning cerebral edema image analysis and processing CT scan CSF volume |
| title | Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients |
| title_full | Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients |
| title_fullStr | Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients |
| title_full_unstemmed | Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients |
| title_short | Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients |
| title_sort | application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients |
| topic | ischemic stroke machine learning cerebral edema image analysis and processing CT scan CSF volume |
| url | https://www.frontiersin.org/article/10.3389/fneur.2018.00687/full |
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