title: From histology and imaging data to models for in-stent restenosis.
abstract: The implantation of stents has been used to treat coronary artery stenosis for several decades. Although stenting is successful in restoring the vessel lumen and is a minimally invasive approach, the long-term outcomes are often compromised by in-stent restenosis (ISR). Animal models have provided insights into the pathophysiology of ISR and are widely used to evaluate candidate drug inhibitors of ISR. Such biological models allow the response of the vessel to stent implantation to be studied without the variation of lesion characteristics encountered in patient studies.This paper describes the development of complementary in silico models employed to improve the understanding of the biological response to stenting using a porcine model of restenosis. This includes experimental quantification using microCT imaging and histology and the use of this data to establish numerical models of restenosis. Comparison of in silico results with histology is used to examine the relationship between spatial localization of fluid and solid mechanics stimuli immediately post-stenting. Multi-scale simulation methods are employed to study the evolution of neointimal growth over time and the variation in the extent of neointimal hyperplasia within the stented region. Interpretation of model results through direct comparison with the biological response contributes to more detailed understanding of the pathophysiology of ISR, and suggests the focus for follow-up studies.In conclusion we outline the challenges which remain to both complete our understanding of the mechanisms responsible for restenosis and translate these models to applications in stent design and treatment planning at both population-based and patient-specific levels.
authors.affiliation: *These authors contributed equally to this publication.
authors.forename: Claudia M
authors.initials: CM
authors.lastname: Amatruda
authors.forename: Carles
authors.initials: C
authors.lastname: Bona Casas
authors.forename: Brandis K
authors.initials: BK
authors.lastname: Keller
authors.forename: Hannan
authors.initials: H
authors.lastname: Tahir
authors.forename: Gabriele
authors.initials: G
authors.lastname: Dubini
authors.forename: Alfons
authors.initials: A
authors.lastname: Hoekstra
authors.forename: D Rodney
authors.initials: DR
authors.lastname: Hose
authors.forename: Patricia
authors.initials: P
authors.lastname: Lawford
authors.forename: Francesco
authors.initials: F
authors.lastname: Migliavacca
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Narracott
authors.forename: Julian
authors.initials: J
authors.lastname: Gunn
mesh_terms: D000818:Animals; D003198:Computer Simulation; D023921:Coronary Stenosis / Q000601:surgery*; D006083:Graft Occlusion, Vascular / Q000628:therapy*; D006801:Humans; D008955:Models, Cardiovascular; D015607:Stents; D013552:Swine