Assessing the role of volumetric brain information in multiple sclerosis progression
Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonan...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001667 |
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| author | Andy A. Shen Aidan McLoughlin Zoe Vernon Jonathan Lin Richard A.D. Carano Peter J. Bickel Zhuang Song Haiyan Huang |
| author_facet | Andy A. Shen Aidan McLoughlin Zoe Vernon Jonathan Lin Richard A.D. Carano Peter J. Bickel Zhuang Song Haiyan Huang |
| author_sort | Andy A. Shen |
| collection | DOAJ |
| description | Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool. |
| format | Article |
| id | doaj-art-4f3b0f1e5b10463eaea7ecf594149b2a |
| institution | OA Journals |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-4f3b0f1e5b10463eaea7ecf594149b2a2025-08-20T02:26:10ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01272014203310.1016/j.csbj.2025.05.003Assessing the role of volumetric brain information in multiple sclerosis progressionAndy A. Shen0Aidan McLoughlin1Zoe Vernon2Jonathan Lin3Richard A.D. Carano4Peter J. Bickel5Zhuang Song6Haiyan Huang7Department of Statistics, UC Berkeley, Berkeley, CA, USADivision of Biostatistics, UC Berkeley, Berkeley, CA, USADepartment of Statistics, UC Berkeley, Berkeley, CA, USADepartment of Statistical Science, Duke University, Durham, NC, USAAnalytics and Medical Imaging, Product Development, Genentech Inc., South San Francisco, CA, USADepartment of Statistics, UC Berkeley, Berkeley, CA, USA; Division of Biostatistics, UC Berkeley, Berkeley, CA, USAAnalytics and Medical Imaging, Product Development, Genentech Inc., South San Francisco, CA, USA; Z. Song and H. Huang are co-last, co-corresponding authors.Department of Statistics, UC Berkeley, Berkeley, CA, USA; Division of Biostatistics, UC Berkeley, Berkeley, CA, USA; Z. Song and H. Huang are co-last, co-corresponding authors.; Corresponding author at: 367 Evans Hall, Berkeley, CA 94720, USA.Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool.http://www.sciencedirect.com/science/article/pii/S2001037025001667Multiple sclerosisDeformation-based morphometrySurvival analysisFeature selectionRandom forestConvolutional neural network |
| spellingShingle | Andy A. Shen Aidan McLoughlin Zoe Vernon Jonathan Lin Richard A.D. Carano Peter J. Bickel Zhuang Song Haiyan Huang Assessing the role of volumetric brain information in multiple sclerosis progression Computational and Structural Biotechnology Journal Multiple sclerosis Deformation-based morphometry Survival analysis Feature selection Random forest Convolutional neural network |
| title | Assessing the role of volumetric brain information in multiple sclerosis progression |
| title_full | Assessing the role of volumetric brain information in multiple sclerosis progression |
| title_fullStr | Assessing the role of volumetric brain information in multiple sclerosis progression |
| title_full_unstemmed | Assessing the role of volumetric brain information in multiple sclerosis progression |
| title_short | Assessing the role of volumetric brain information in multiple sclerosis progression |
| title_sort | assessing the role of volumetric brain information in multiple sclerosis progression |
| topic | Multiple sclerosis Deformation-based morphometry Survival analysis Feature selection Random forest Convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001667 |
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