Theranostics 2018; 8(22):6384-6385. doi:10.7150/thno.30753

Editorial

Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment

Vanessa Diaz-Zuccarini1,2✉, Mirko Bonfanti2,1, Gaia Franzetti1, Stavroula Balabani1

1. University College London, Department of Mechanical Engineering, Multiscale Cardiovascular Engineering Group, UK
2. WEISS Centre for Surgical and Interventional Sciences, University College London, UK.

Abstract

The use of in silico tools for the interventional planning of complex vascular conditions, such as Aortic Dissections has been often limited by high computational cost, involving long timescales for accurate results to be produced and low numbers of patients, precluding the use of statistical analyses to inform individual-level models. In the paper [Theranostics 2018; 8(20):5758-5771. doi:10.7150/thno.28944], Chen et al. proposed a novel algorithm to compute patient-specific 'virtual TEVAR' that will help clinicians to approach individual treatment and decision-making based on objective and quantifiable metrics and validated on a cohort of 66 patients in real time. This research will significantly impact the field and has the potential to transform the way clinical interventions will be approached in the future.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
How to cite this article:
Diaz-Zuccarini V, Bonfanti M, Franzetti G, Balabani S. Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment. Theranostics 2018; 8(22):6384-6385. doi:10.7150/thno.30753. Available from http://www.thno.org/v08p6384.htm