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

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.

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 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.

AORTLANTIC: French registry of aortic valve-sparing root replacement, preliminary multicentre results from western France.

OBJECTIVES: Aortic root aneurysms often affect younger patients in whom valve-sparing surgery is challenging. Among current techniques, aortic valve-sparing root replacement described by Tirone David has shown encouraging results. The AORTLANTIC registry was instituted for a multicentre long-term evaluation of this procedure. The current initial study evaluates the hospital outcomes of the procedure. METHODS: This is a retrospective study of patients operated between 1 January 2004 and 31 December 2020 in 6 hospitals in western France. All study data were recorded in the national digital database of the French Society of Cardiac Surgery: EPICARD. RESULTS: A total of 524 consecutive patients with a mean age of 53 (15.1) years underwent surgery. 13% (n = 68) of patients presented with acute aortic dissection, 16.5% (n = 86) had associated connective tissue pathology and 7.3% (n = 37) had bicuspid aortic valves. Preoperative aortic regurgitation (AR) ≥2/4 was present in 65.3% (n = 341) of patients. Aortic valvuloplasty was required in 18.6% (n = 95) of patients. At discharge, 92.8% (n = 461) of patients had no or 1/4 AR. The stroke rate was 1.9% (n = 10). Intra-hospital mortality was 1.9% (n = 10). CONCLUSIONS: The AORTLANTIC registry includes 6 centres in western France with >500 patients. Despite numerous complex cases (acute aortic dissections, bicuspid aortic valves, preoperative AR), aortic valve-sparing root replacement has a low intra-hospital mortality. The initial encouraging results of this multicentre study warrant further long-term evaluation by future studies.