Evolving paradigms in aortic dissection from morphological predictors to computer-aided diagnosis

Background. Aortic dissection is characterized by the partial detachment of the inner layer of aortic wall, leading to the creation of two different channels inside the aorta. Depending on the configuration of the dissection, the prognosis varies from elevated risk of lethal complications to stable course. However, the indication for treatment is not always univocal. Some uncomplicated type B dissections may present an increased risk profile and thus may benefit from early intervention. A thorough understanding of the case-specific three-dimensional shape is crucial in order to judge the prognosis and plan the procedure. Many studies have attempted to use the morphological features of the aorta to predict the evolution of uncomplicated dissections during follow-up. Objectives. The aims of this study are: to assess the state of the art of morphological predictors of adverse outcome in type B dissections; to better define the role of thoracic endovascular aortic repair (TEVAR) for uncomplicated dissections in current clinical practice; to explore the feasibility of applying novel computational technologies in the the study of the vascular morphology. Methods and results. A systematic review of the literature on morphological predictors of aortic growth and adverse events in type B dissection was carried out. Results in terms of outcome prediction are varied and sometimes conflicting. Data from the most important international registries on aortic dissection were analysed. Outcomes of uncomplicated patients treated by best medical therapy alone were compared to the ones of best medical therapy associated with TEVAR. Then an analysis of patients treated with TEVAR for complicated versus uncomplicated type B aortic dissection was carried out. Finally, advanced semi-automated segmentation algorithms have been applied to computed tomography-angiography imaging of aortic disease in order to extract the three-dimensional shape of aortic lumen. This allowed to produce patient-specific models of aortic disease in a transparent rigid resin through Vat-photopolymerization technique. These models were used for preoperative rehearsal of complex cases by the treating surgeons and to improve the spatial understanding of aortic disease by the surgical trainees and medical students. Conclusion. Analysis of vascular imaging through computational techniques, including 3D printing, is feasible and will likely pave the way for novel approaches of outcome prediction in aortic dissection.