P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis
Introduction: Many rare disorders, particularly neurodevelopmental conditions, manifest structural brain malformations. Just as dysmorphologists rely on facial gestalt recognition to identify syndromes, radiologists and neurologists face similar challenges in identifying the ''brain gestal...
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Elsevier
2025-01-01
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| Series: | Genetics in Medicine Open |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949774425014633 |
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| author | Tzung-Chien Hsieh Shriya Jaddu Hannah Weiland Merle ten Hagen Jing-Mei Li Chi-Chia Chang Sun-Yuan Hsieh Hsin-Hung Chou Gholson Lyon William Dobyns Wei-Liang Chen |
| author_facet | Tzung-Chien Hsieh Shriya Jaddu Hannah Weiland Merle ten Hagen Jing-Mei Li Chi-Chia Chang Sun-Yuan Hsieh Hsin-Hung Chou Gholson Lyon William Dobyns Wei-Liang Chen |
| author_sort | Tzung-Chien Hsieh |
| collection | DOAJ |
| description | Introduction: Many rare disorders, particularly neurodevelopmental conditions, manifest structural brain malformations. Just as dysmorphologists rely on facial gestalt recognition to identify syndromes, radiologists and neurologists face similar challenges in identifying the ''brain gestalt'' of rare disorders—especially when encountering rare conditions or those they have not previously seen. Next-generation phenotyping (NGP) has been proven capable of supporting clinicians in recognizing facial dysmorphic patterns associated with the underlying syndrome through training on thousands of patient photographs. Beyond facial image analysis, NGP can also be applied to brain MRI data to identify structural malformations, such as Dandy-Walker malformation and lissencephaly, by learning patterns from large datasets of brain MRI images. In this work, we propose a deep learning-based NGP approach to detect brain malformations and their associated disorders, providing clinicians with diagnostic support and enabling integration into variant prioritization pipelines. Methods: We curated a dataset of 413 brain MRI images from publications and clinicians, covering 56 different disorders, and stored it in the GestaltMatcher Database (GMDB). To learn the brain structures from MRI, we applied transfer learning using ResNet-50, pre-trained on the fastMRI dataset from NYU School of Medicine, comprising 6,970 MRIs for age prediction. This model was then used to encode each MRI into a high-dimensional feature vector, creating the ''Clinical Brain Phenotype Space (CBPS).'' In CBPS, each MRI is represented as a point, where proximity indicates phenotypic similarity between brains. To refine our focus on pediatric brains, we encoded 883 MRIs from a public dataset and 396 from the Preschool MRI dataset. We used feature-space distances to measure the probability of associated disorders. Results: We evaluated our approach on two conditions: Dandy-Walker malformation and Ogden syndrome. In CBPS, we successfully distinguished both conditions from healthy controls by leave-one-out cross-validation. When visualized using t-SNE, patients with Dandy-Walker malformation formed a distinct cluster. At the same time, patients with Ogden syndrome also demonstrated clear separation from controls, validating the potential of CBPS for phenotypic clustering and disease prediction. Conclusion: This study demonstrates the application of NGP to structural brain malformations in rare disorders. While our initial analysis focused on two specific conditions, the results highlight the feasibility of extending this approach to a broader spectrum of genetic disorders. With ongoing data curation and patient recruitment through the GMDB consortium, we envision scaling this work to encompass hundreds of disorders, thereby advancing the diagnosis and understanding of rare conditions on a global scale. |
| format | Article |
| id | doaj-art-a3e7b400dcd64b60b37d89042dcdf9e6 |
| institution | Kabale University |
| issn | 2949-7744 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Genetics in Medicine Open |
| spelling | doaj-art-a3e7b400dcd64b60b37d89042dcdf9e62025-08-20T03:42:52ZengElsevierGenetics in Medicine Open2949-77442025-01-01310342410.1016/j.gimo.2025.103424P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysisTzung-Chien Hsieh0Shriya Jaddu1Hannah Weiland2Merle ten Hagen3Jing-Mei Li4Chi-Chia Chang5Sun-Yuan Hsieh6Hsin-Hung Chou7Gholson Lyon8William Dobyns9Wei-Liang Chen10Institute for Genomic Statistics and Bioinformatics, University Hospital of Bonn, Bonn, GermanyDepartment of Pediatric Neurology, Children’s National Hospital, Washington, DCInstitute for Genomic Statistics and Bioinformatics, University Hospital of Bonn, Bonn, GermanyInstitute for Genomic Statistics and Bioinformatics, University Hospital of Bonn, Bonn, GermanyInstitute for Genomic Statistics and Bioinformatics, University Hospital of Bonn, Bonn, GermanyDepartment of Computer Science and Information Engineering, National Cheng Kung University, TainanDepartment of Computer Science and Information Engineering, National Cheng Kung University, TainanDepartment of Computer Science and Information Engineering, National Chi Nan University, NantouDepartment of Human Genetics, New York State Institute for Basic Research in Developmental DisabilitiesUniversity of Minnesota, Pediatric Genetics & Metabolism, Minneapolis, MNDepartment of Pediatric Neurology, Children’s National Hospital, Washington, DCIntroduction: Many rare disorders, particularly neurodevelopmental conditions, manifest structural brain malformations. Just as dysmorphologists rely on facial gestalt recognition to identify syndromes, radiologists and neurologists face similar challenges in identifying the ''brain gestalt'' of rare disorders—especially when encountering rare conditions or those they have not previously seen. Next-generation phenotyping (NGP) has been proven capable of supporting clinicians in recognizing facial dysmorphic patterns associated with the underlying syndrome through training on thousands of patient photographs. Beyond facial image analysis, NGP can also be applied to brain MRI data to identify structural malformations, such as Dandy-Walker malformation and lissencephaly, by learning patterns from large datasets of brain MRI images. In this work, we propose a deep learning-based NGP approach to detect brain malformations and their associated disorders, providing clinicians with diagnostic support and enabling integration into variant prioritization pipelines. Methods: We curated a dataset of 413 brain MRI images from publications and clinicians, covering 56 different disorders, and stored it in the GestaltMatcher Database (GMDB). To learn the brain structures from MRI, we applied transfer learning using ResNet-50, pre-trained on the fastMRI dataset from NYU School of Medicine, comprising 6,970 MRIs for age prediction. This model was then used to encode each MRI into a high-dimensional feature vector, creating the ''Clinical Brain Phenotype Space (CBPS).'' In CBPS, each MRI is represented as a point, where proximity indicates phenotypic similarity between brains. To refine our focus on pediatric brains, we encoded 883 MRIs from a public dataset and 396 from the Preschool MRI dataset. We used feature-space distances to measure the probability of associated disorders. Results: We evaluated our approach on two conditions: Dandy-Walker malformation and Ogden syndrome. In CBPS, we successfully distinguished both conditions from healthy controls by leave-one-out cross-validation. When visualized using t-SNE, patients with Dandy-Walker malformation formed a distinct cluster. At the same time, patients with Ogden syndrome also demonstrated clear separation from controls, validating the potential of CBPS for phenotypic clustering and disease prediction. Conclusion: This study demonstrates the application of NGP to structural brain malformations in rare disorders. While our initial analysis focused on two specific conditions, the results highlight the feasibility of extending this approach to a broader spectrum of genetic disorders. With ongoing data curation and patient recruitment through the GMDB consortium, we envision scaling this work to encompass hundreds of disorders, thereby advancing the diagnosis and understanding of rare conditions on a global scale.http://www.sciencedirect.com/science/article/pii/S2949774425014633 |
| spellingShingle | Tzung-Chien Hsieh Shriya Jaddu Hannah Weiland Merle ten Hagen Jing-Mei Li Chi-Chia Chang Sun-Yuan Hsieh Hsin-Hung Chou Gholson Lyon William Dobyns Wei-Liang Chen P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis Genetics in Medicine Open |
| title | P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis |
| title_full | P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis |
| title_fullStr | P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis |
| title_full_unstemmed | P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis |
| title_short | P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis |
| title_sort | p253 next generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain mri analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2949774425014633 |
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