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

Abstract

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.

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

“…Journal Pre-proof of the ascending tract. This agrees with the correlation study between local shape features and growth reported in our previous work [42] and with the shape representation provided by the PLS modes.…”

“…To increase the likelihood of producing reliable and reproducible results, additional exclusion criteria were set: (1) patients under 25 years of age, (2) aneurysms related to systemic inflammatory diseases, (3) prior aortic valve surgery, (4) acute aortic syndrome, (5) 110 congenital tissue disorders such as Marfan syndrome and (6) images damaged by artefacts. In total, N = 70 patients were included, 50 of whom had already been used in our previous work related to the identification of local shape features [42]. 47 (67.1%) patients had double ECG-gated acquisitions.…”

“…The external curvature line (ECL) was computed by projecting each point gen-145 erating the ICL onto the external part of the aorta in a direction perpendicular to the centerline and passing through it [42]. Using the geometries derived from the first acquisition, the following local shape features, shown in Fig.…”

“…The study about the correlation between the local shape features here proposed 165 and the aneurysm growth rate has already been performed in a previous work [42].…”

“…In this paper, using the geometric decomposition techniques presented in our previous work [42], we first derive a set of local shape features using a more extensive dataset than the one previously exploited. After, we perform statistical 75 shape analysis to extract global shape features from principal component analysis and partial least squares.…”