P021. A Semi-Automated Method to Obtain Metrics of Interest to Evaluate an Ascending Aortic Aneurysm Evolution
Universtity Hospital of Rennes
Rennes, Bretagne
France - Contact Me
I am Dr. Jacques Tomasi, a 39 year-old Cardiovascular Surgeon currently serving as a Senior consultant at the University Hospital of Rennes, France. Working under the leadership of Professor Verhoye has been a privilege and a significant influence on my career in cardiovascular surgery. As the chairman at our institution, Professor Verhoye is not only a respected figure locally but is also extensively involved nationwide in advancing the field of cardiovascular surgery.
I had the privilege of completing a fellowship at the renowned Kawasaki Aortic Center, an experience that significantly shaped my perspective and skills in the field.During my fellowship in Kawasaki, I had the opportunity to immerse myself in the advanced practices and techniques at the Aortic Center. This invaluable experience has been instrumental in shaping my approach to cardiovascular surgery, particularly in the realm of aortic procedures.
Now, as a Senior consultant, I am deeply committed to contributing to the field of cardiovascular surgery. My focus remains on advancing our understanding and treatment of aortic pathologies, with a specific interest in open aortic procedures. My experiences continue to influence my practice, reminding me of the importance of continuous learning and collaboration.
In my current role, I am grateful for the opportunity to share my knowledge and experiences with colleagues and contribute to the collective expertise in cardiovascular surgery. I approach my work with humility and a genuine dedication to the well-being of my patients.
As I look forward to the upcoming congress, I am eager to share insights from my experiences and my current works in progress in the field of computer-helped simulation or surgery. My aim is to contribute to the discussions in a meaningful way, recognizing that each contribution, no matter how small, plays a part in advancing our field
Description
We studied a cohort of ninety-one patients with a follow-up of ascending aortic aneurysm using CT-scan. For each patient, we evaluated with semi-automated method diameter but also less often used measurements such as centerline, external and internal curvature, surface and volume. We also implemented original metrics helped by "morphing process" : strain over time and local growth area using a computer-helped method.
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).
Our method provided for each patient diameter along centerline but also lenghts of external, internal curvatures, surface, and volume. Strain and local growth area were obtained for four patients. These different parameters seem to increase with aneurysmal dilatation.
The workflow presented enables to obtain metrics of interests which seem correlated to aneurysmal evolution. Further studies are needed to assess correlation of these metrics with risk of acute complications such as rupture or aortic dissection. Also these parameters does not take into account for now the arterial pressure that can have potential effect in aneurysm evolution.
Authors
Jacques Tomasi (1), Pierre Flores (2), Leonardo Geronzi (3), Albadi Waleed (2), Pascal Haigron (4), Jean-Philippe Verhoye (5)
Institutions
(1) Universtity Hospital of Rennes, Rennes, France, (2) University Hospital of Rennes, Rennes, France, (3) University of Roma, Roma, Italy, (4) University of Rennes, Rennes, France, (5) University hospital of Rennes, Rennes, France