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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.04309 |
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| _version_ | 1866914368701071360 |
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| author | Kulkarni, Pranav Lal, Brajesh K. Jreij, Georges Vallamchetla, Sai Green, Langford Voeks, Jenifer Huston, John Edwards, Lloyd Howard, George Maron, Bradley A. Brott, Thomas G. Meschia, James F. Doo, Florence X. Huang, Heng |
| author_facet | Kulkarni, Pranav Lal, Brajesh K. Jreij, Georges Vallamchetla, Sai Green, Langford Voeks, Jenifer Huston, John Edwards, Lloyd Howard, George Maron, Bradley A. Brott, Thomas G. Meschia, James F. Doo, Florence X. Huang, Heng |
| contents | Accurate characterization of carotid plaques is critical for stroke prevention in patients with carotid stenosis. We analyze 500 plaques from CREST-2, a multi-center clinical trial, to identify radiomics-based markers from B-mode ultrasound images linked with high-risk. We propose a new kernel-based additive model, combining coherence loss with group-sparse regularization for nonlinear classification. Group-wise additive effects of each feature group are visualized using partial dependence plots. Results indicate our method accurately and interpretably assesses plaques, revealing a strong association between plaque texture and clinical risk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04309 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model Kulkarni, Pranav Lal, Brajesh K. Jreij, Georges Vallamchetla, Sai Green, Langford Voeks, Jenifer Huston, John Edwards, Lloyd Howard, George Maron, Bradley A. Brott, Thomas G. Meschia, James F. Doo, Florence X. Huang, Heng Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Accurate characterization of carotid plaques is critical for stroke prevention in patients with carotid stenosis. We analyze 500 plaques from CREST-2, a multi-center clinical trial, to identify radiomics-based markers from B-mode ultrasound images linked with high-risk. We propose a new kernel-based additive model, combining coherence loss with group-sparse regularization for nonlinear classification. Group-wise additive effects of each feature group are visualized using partial dependence plots. Results indicate our method accurately and interpretably assesses plaques, revealing a strong association between plaque texture and clinical risk. |
| title | CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.04309 |