AUTHOR=Geronzi Leonardo , Haigron Pascal , Martinez Antonio , Yan Kexin , Rochette Michel , Bel-Brunon Aline , Porterie Jean , Lin Siyu , Marin-Castrillon Diana Marcela , Lalande Alain , Bouchot Olivier , Daniel Morgan , Escrig Pierre , Tomasi Jacques , Valentini Pier Paolo , Biancolini Marco Evangelos 

TITLE=Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

JOURNAL=Frontiers in Physiology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1125931

DOI=10.3389/fphys.2023.1125931

ISSN=1664-042X

ABSTRACT=<p>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 <italic>D</italic>, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from <italic>D</italic>, these are the ratio <italic>DCR</italic> between <italic>D</italic> and the length of the ascending aorta centerline, the ratio <italic>EILR</italic> between the length of the external and the internal lines and the tortuosity <italic>T</italic>. 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 <italic>D</italic> 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 <italic>DCR</italic> (<italic>r</italic> = 0.511, <italic>p</italic> = 1.3e-4) and between GR and <italic>EILR</italic> (<italic>r</italic> = 0.472, <italic>p</italic> = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on <italic>D</italic>. 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+ (+<italic>∞</italic>) 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.</p>