Assessment of shape-based features ability to predict the ascending aortic aneurysm growth
The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1125931/full |
| _version_ | 1811159560191213568 |
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| author | Leonardo Geronzi Leonardo Geronzi Pascal Haigron Antonio Martinez Antonio Martinez Kexin Yan Kexin Yan Michel Rochette Aline Bel-Brunon Jean Porterie Siyu Lin Siyu Lin Diana Marcela Marin-Castrillon Diana Marcela Marin-Castrillon Alain Lalande Alain Lalande Olivier Bouchot Morgan Daniel Pierre Escrig Jacques Tomasi Pier Paolo Valentini Marco Evangelos Biancolini |
| author_facet | Leonardo Geronzi Leonardo Geronzi Pascal Haigron Antonio Martinez Antonio Martinez Kexin Yan Kexin Yan Michel Rochette Aline Bel-Brunon Jean Porterie Siyu Lin Siyu Lin Diana Marcela Marin-Castrillon Diana Marcela Marin-Castrillon Alain Lalande Alain Lalande Olivier Bouchot Morgan Daniel Pierre Escrig Jacques Tomasi Pier Paolo Valentini Marco Evangelos Biancolini |
| author_sort | Leonardo Geronzi |
| collection | DOAJ |
| description | The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR−). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR− (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR− (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease. |
| first_indexed | 2024-04-10T05:44:22Z |
| format | Article |
| id | doaj.art-87dcaff65c494ca39c138661869af8be |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-10T05:44:22Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Physiology |
| spelling | doaj.art-87dcaff65c494ca39c138661869af8be2023-03-06T05:15:42ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-03-011410.3389/fphys.2023.11259311125931Assessment of shape-based features ability to predict the ascending aortic aneurysm growthLeonardo Geronzi0Leonardo Geronzi1Pascal Haigron2Antonio Martinez3Antonio Martinez4Kexin Yan5Kexin Yan6Michel Rochette7Aline Bel-Brunon8Jean Porterie9Siyu Lin10Siyu Lin11Diana Marcela Marin-Castrillon12Diana Marcela Marin-Castrillon13Alain Lalande14Alain Lalande15Olivier Bouchot16Morgan Daniel17Pierre Escrig18Jacques Tomasi19Pier Paolo Valentini20Marco Evangelos Biancolini21Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, ItalyAnsys France, Villeurbanne, FranceLTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, FranceDepartment of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, ItalyAnsys France, Villeurbanne, FranceAnsys France, Villeurbanne, FranceLaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, FranceAnsys France, Villeurbanne, FranceLaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, FranceCardiac Surgery Department, Rangueil University Hospital, Toulouse, FranceIMVIA Laboratory, University of Burgundy, Dijon, FranceMedical Imaging Department, University Hospital of Dijon, Dijon, FranceIMVIA Laboratory, University of Burgundy, Dijon, FranceMedical Imaging Department, University Hospital of Dijon, Dijon, FranceIMVIA Laboratory, University of Burgundy, Dijon, FranceMedical Imaging Department, University Hospital of Dijon, Dijon, FranceDepartment of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, FranceLTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, FranceLTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, FranceLTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, FranceDepartment of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, ItalyDepartment of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, ItalyThe current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR−). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR− (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR− (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.https://www.frontiersin.org/articles/10.3389/fphys.2023.1125931/fullcardiovascular diseasesascending aorta aneurysmbiomechanical featuresclassificationaortamachine learning |
| spellingShingle | Leonardo Geronzi Leonardo Geronzi Pascal Haigron Antonio Martinez Antonio Martinez Kexin Yan Kexin Yan Michel Rochette Aline Bel-Brunon Jean Porterie Siyu Lin Siyu Lin Diana Marcela Marin-Castrillon Diana Marcela Marin-Castrillon Alain Lalande Alain Lalande Olivier Bouchot Morgan Daniel Pierre Escrig Jacques Tomasi Pier Paolo Valentini Marco Evangelos Biancolini Assessment of shape-based features ability to predict the ascending aortic aneurysm growth Frontiers in Physiology cardiovascular diseases ascending aorta aneurysm biomechanical features classification aorta machine learning |
| title | Assessment of shape-based features ability to predict the ascending aortic aneurysm growth |
| title_full | Assessment of shape-based features ability to predict the ascending aortic aneurysm growth |
| title_fullStr | Assessment of shape-based features ability to predict the ascending aortic aneurysm growth |
| title_full_unstemmed | Assessment of shape-based features ability to predict the ascending aortic aneurysm growth |
| title_short | Assessment of shape-based features ability to predict the ascending aortic aneurysm growth |
| title_sort | assessment of shape based features ability to predict the ascending aortic aneurysm growth |
| topic | cardiovascular diseases ascending aorta aneurysm biomechanical features classification aorta machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1125931/full |
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