Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Geronzi, Leonardo Martinez, Antonio Rochette, Michel Yan, Kexin Bel-Brunon, Aline Haigron, Pascal Escrig, Pierre Tomasi, Jacques Daniel, Morgan Lalande, Alain Lin, Siyu Marin-Castrillon, Diana Marcela Bouchot, Olivier Porterie, Jean Valentini, Pier Paolo Biancolini, Marco Evangelos |
| author_facet | Geronzi, Leonardo Martinez, Antonio Rochette, Michel Yan, Kexin Bel-Brunon, Aline Haigron, Pascal Escrig, Pierre Tomasi, Jacques Daniel, Morgan Lalande, Alain Lin, Siyu Marin-Castrillon, Diana Marcela Bouchot, Olivier Porterie, Jean Valentini, Pier Paolo Biancolini, Marco Evangelos |
| contents | Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth.
Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.
Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.
Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate Geronzi, Leonardo Martinez, Antonio Rochette, Michel Yan, Kexin Bel-Brunon, Aline Haigron, Pascal Escrig, Pierre Tomasi, Jacques Daniel, Morgan Lalande, Alain Lin, Siyu Marin-Castrillon, Diana Marcela Bouchot, Olivier Porterie, Jean Valentini, Pier Paolo Biancolini, Marco Evangelos Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Numerical Analysis Medical Physics Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth. |
| title | Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Numerical Analysis Medical Physics |
| url | https://arxiv.org/abs/2503.02915 |