Predicting the Influence of Microvascular Structure On Tumor Response to Radiotherapy.
The purpose of this study is to investigate how theoretical predictions of tumor response to radiotherapy (RT) depend on the morphology and spatial representation of the microvascular network.



A hybrid multiscale model, which couples a cellular automaton model of tumor growth with a model for oxygen transport from blood vessels, is used to predict the viable fraction of cells following one week of simulated RT. Both artificial and biologically derived three-dimensional (3-D) vessel networks of well vascularized tumors are considered and predictions compared with 2-D descriptions.



For literature-derived values of the cellular oxygen consumption rate there is little difference in predicted viable fraction when 3-D network representations of biological or artificial vessel networks are employed. Different 2-D representations are shown to either over- or under-estimate viable fractions relative to the 3-D cases, with predictions based on point-wise descriptions shown to have greater sensitivity to vessel network morphology.



The predicted RT response is relatively insensitive to the morphology of the microvessel network when 3-D representations are adopted, however, sensitivity is greater in certain 2-D representations.



By using realistic 3-D vessel network geometries this study shows that real and artificial network descriptions and assumptions of spatially uniform oxygen distributions lead to similar RT response predictions in relatively small tissue volumes. This suggests that either a more detailed description of oxygen transport in the microvasculature is required or that the oxygen enhancement ratio used in the well known linear-quadratic RT response model is relatively insensitive to microvascular structure.
Mathematical Institute, University of Oxford, Oxford, U.K.
James A
JA
Grogan
Department of OncologyCRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford.
Bostjan
B
Markelc
Department of Computer ScienceUniversity of Oxford.
Anthony J
AJ
Connor
Department of OncologyCRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford.
Ruth J
RJ
Muschel
Department of Computer ScienceUniversity of Oxford.
Joe M
JM
Pitt-Francis
Mathematical Institute, University of Oxford.
Philip K
PK
Maini
Mathematical Institute, University of Oxford.
Helen M
HM
Byrne
D000818:Animals; D003198:Computer Simulation; D006801:Humans; D055806:Microvessels / Q000378:metabolism* / Q000473:pathology; D008954:Models, Biological; D009369:Neoplasms / Q000098:blood supply / Q000378:metabolism* / Q000532:radiotherapy*; D009389:Neovascularization, Pathologic / Q000378:metabolism* / Q000473:pathology / Q000517:prevention & control; D010100:Oxygen / Q000097:blood*; D011379:Prognosis; D016896:Treatment Outcome; D000072258:Tumor Hypoxia / Q000502:physiology / Q000528:radiation effects
D010100:Oxygen