دورية أكاديمية
أعرض في EDS

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate.

التفاصيل البيبلوغرافية
العنوان: Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate.
المؤلفون: Geronzi L; University of Rome Tor Vergata, Department of Enterprise Engineering 'Mario Lucertini', Rome, Italy; Ansys France, Villeurbanne, France. Electronic address: leonardo.geronzi@uniroma2.it., Martinez A; University of Rome Tor Vergata, Department of Enterprise Engineering 'Mario Lucertini', Rome, Italy; Ansys France, Villeurbanne, France., Rochette M; Ansys France, Villeurbanne, France., Yan K; Ansys France, Villeurbanne, France; University of Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France., Bel-Brunon A; University of Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France., Haigron P; University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France., Escrig P; University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France., Tomasi J; University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France., Daniel M; University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France., Lalande A; ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France., Lin S; ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France., Marin-Castrillon DM; ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France., Bouchot O; Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France., Porterie J; Cardiac Surgery Department, Rangueil University Hospital, Toulouse, France., Valentini PP; University of Rome Tor Vergata, Department of Enterprise Engineering 'Mario Lucertini', Rome, Italy., Biancolini ME; University of Rome Tor Vergata, Department of Enterprise Engineering 'Mario Lucertini', Rome, Italy.
المصدر: Computers in biology and medicine [Comput Biol Med] 2023 Aug; Vol. 162, pp. 107052. Date of Electronic Publication: 2023 May 25.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
أسماء مطبوعة: Publication: New York : Elsevier
Original Publication: New York, Pergamon Press.
مواضيع طبية MeSH: Aneurysm, Ascending Aorta* , Aortic Aneurysm*, Humans ; Aorta/diagnostic imaging ; Retrospective Studies
مستخلص: 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 the 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.
Competing Interests: Declaration of Competing Interest During the development of the work, Leonardo Geronzi, Antonio Martinez, Kexin Yan and Michel Rochette were employed by Ansys France. The other authors have no commercial, proprietary, or financial relationships that could be construed as a potential conflict of interest. In any case, there has been no financial support for this work that could have influenced its outcome.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
فهرسة مساهمة: Keywords: Ascending aortic aneurysm; Growth prediction; Regression; Shape features
تواريخ الأحداث: Date Created: 20230601 Date Completed: 20230619 Latest Revision: 20231121
رمز التحديث: 20240628
DOI: 10.1016/j.compbiomed.2023.107052
PMID: 37263151
قاعدة البيانات: MEDLINE
الوصف
تدمد:1879-0534
DOI:10.1016/j.compbiomed.2023.107052