A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia
Canine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and neuroparenchyma, resulting in pain (Chiari associated pain or CM-P) and the development of syringomyelia (SM). The associated neuro-disability in affe...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2024.1492259/full |
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| author | Jake Cumber Jake Cumber Emma Scales-Theobald Emma Scales-Theobald Clare Rusbridge Clare Rusbridge Clare Rusbridge Kevin Wells Kevin Wells |
| author_facet | Jake Cumber Jake Cumber Emma Scales-Theobald Emma Scales-Theobald Clare Rusbridge Clare Rusbridge Clare Rusbridge Kevin Wells Kevin Wells |
| author_sort | Jake Cumber |
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| description | Canine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and neuroparenchyma, resulting in pain (Chiari associated pain or CM-P) and the development of syringomyelia (SM). The associated neuro-disability in affected individuals compromises quality of life. CM is characterized by overcrowding of the brain and cervical spinal cord and is predisposed by skull-base shortening and miniaturization with brachycephalic toy dogs overwhelmingly represented. Magnetic resonance imaging (MRI) is conventionally used for diagnosis; however, CM is complex and ubiquitous in some dog breeds so that diagnosis of CM-P relies on a combination of clinical signs, MRI, and elimination of other causes of pain. This research aimed to identify cranial and spinal pathologies and neural morphologies linked to CM-P and SM in dogs using MRI scans and machine learning with the aim of identifying novel data driven biomarkers which could confirm CM-P and identify dogs at risk of developing SM. The methodology identified four regions of interest as having robust discrimination for CM-P, with 89% sensitivity and 76% specificity. A set of morphological features linked to CM-P were identified. Four regions of interest were also identified as having robust discrimination for SM, with 84% sensitivity and 80% specificity. Overall, these findings shed light on the distinct morphologies related to CM-P and SM, offering the potential for more accurate and objective diagnoses in affected dogs using MRI. These results contribute to the further understanding of the complex pathologies associated with CM and SM in brachycephalic toy pure and mixed breed dogs and support the potential utility of data-driven techniques for advancing our knowledge of these debilitating conditions. |
| format | Article |
| id | doaj-art-a4daef100f6343dda05cbddf301ed8ff |
| institution | Kabale University |
| issn | 2297-1769 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj-art-a4daef100f6343dda05cbddf301ed8ff2025-01-22T14:29:15ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-01-011110.3389/fvets.2024.14922591492259A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyeliaJake Cumber0Jake Cumber1Emma Scales-Theobald2Emma Scales-Theobald3Clare Rusbridge4Clare Rusbridge5Clare Rusbridge6Kevin Wells7Kevin Wells8Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey, Guildford, United KingdomCanine Chiari Group, School of Veterinary Medicine, Faculty of Health and Medical Sciences, Guildford, United KingdomCentre for Vision Speech and Signal Processing (CVSSP), University of Surrey, Guildford, United KingdomCanine Chiari Group, School of Veterinary Medicine, Faculty of Health and Medical Sciences, Guildford, United KingdomCanine Chiari Group, School of Veterinary Medicine, Faculty of Health and Medical Sciences, Guildford, United KingdomFitzpatrick Referrals Orthopaedics and Neurology, Godalming, United KingdomWear Referrals Veterinary Specialist and Emergency Hospital, Stockton-on-Tees, United KingdomCentre for Vision Speech and Signal Processing (CVSSP), University of Surrey, Guildford, United KingdomCanine Chiari Group, School of Veterinary Medicine, Faculty of Health and Medical Sciences, Guildford, United KingdomCanine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and neuroparenchyma, resulting in pain (Chiari associated pain or CM-P) and the development of syringomyelia (SM). The associated neuro-disability in affected individuals compromises quality of life. CM is characterized by overcrowding of the brain and cervical spinal cord and is predisposed by skull-base shortening and miniaturization with brachycephalic toy dogs overwhelmingly represented. Magnetic resonance imaging (MRI) is conventionally used for diagnosis; however, CM is complex and ubiquitous in some dog breeds so that diagnosis of CM-P relies on a combination of clinical signs, MRI, and elimination of other causes of pain. This research aimed to identify cranial and spinal pathologies and neural morphologies linked to CM-P and SM in dogs using MRI scans and machine learning with the aim of identifying novel data driven biomarkers which could confirm CM-P and identify dogs at risk of developing SM. The methodology identified four regions of interest as having robust discrimination for CM-P, with 89% sensitivity and 76% specificity. A set of morphological features linked to CM-P were identified. Four regions of interest were also identified as having robust discrimination for SM, with 84% sensitivity and 80% specificity. Overall, these findings shed light on the distinct morphologies related to CM-P and SM, offering the potential for more accurate and objective diagnoses in affected dogs using MRI. These results contribute to the further understanding of the complex pathologies associated with CM and SM in brachycephalic toy pure and mixed breed dogs and support the potential utility of data-driven techniques for advancing our knowledge of these debilitating conditions.https://www.frontiersin.org/articles/10.3389/fvets.2024.1492259/fullbrachycephalycanineimage registrationmachine learningmorphologiesMRI |
| spellingShingle | Jake Cumber Jake Cumber Emma Scales-Theobald Emma Scales-Theobald Clare Rusbridge Clare Rusbridge Clare Rusbridge Kevin Wells Kevin Wells A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia Frontiers in Veterinary Science brachycephaly canine image registration machine learning morphologies MRI |
| title | A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia |
| title_full | A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia |
| title_fullStr | A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia |
| title_full_unstemmed | A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia |
| title_short | A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia |
| title_sort | data driven approach for soft tissue biomarker identification linked to chiari like malformation and syringomyelia |
| topic | brachycephaly canine image registration machine learning morphologies MRI |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2024.1492259/full |
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