Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers
Abstract This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identifi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | npj Imaging |
| Online Access: | https://doi.org/10.1038/s44303-024-00063-x |
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| _version_ | 1841559184831676416 |
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| author | Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E. Black Alan R. Moody April Khademi |
| author_facet | Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E. Black Alan R. Moody April Khademi |
| author_sort | Karissa Chan |
| collection | DOAJ |
| description | Abstract This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly (p < 0.05) different across subgroups, while Fisher’s tests revealed significant (p < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers. |
| format | Article |
| id | doaj-art-fbcc45fab7cb42c587433fe2fe3be1bc |
| institution | Kabale University |
| issn | 2948-197X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Imaging |
| spelling | doaj-art-fbcc45fab7cb42c587433fe2fe3be1bc2025-01-05T12:43:08ZengNature Portfolionpj Imaging2948-197X2024-12-012111110.1038/s44303-024-00063-xStratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkersKarissa Chan0Corinne Fischer1Pejman Jabehdar Maralani2Sandra E. Black3Alan R. Moody4April Khademi5Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan UniversityKeenan Research Center for Biomedical Science, St. Michael’s Hospital, Unity Health NetworkDepartment of Medical Imaging, University of TorontoInstitute of Medical Science, University of TorontoDepartment of Medical Imaging, University of TorontoDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan UniversityAbstract This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly (p < 0.05) different across subgroups, while Fisher’s tests revealed significant (p < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.https://doi.org/10.1038/s44303-024-00063-x |
| spellingShingle | Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E. Black Alan R. Moody April Khademi Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers npj Imaging |
| title | Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers |
| title_full | Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers |
| title_fullStr | Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers |
| title_full_unstemmed | Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers |
| title_short | Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers |
| title_sort | stratifying vascular disease patients into homogeneous subgroups using machine learning and flair mri biomarkers |
| url | https://doi.org/10.1038/s44303-024-00063-x |
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