TY - JOUR
T1 - Tumor ablation due to inhomogeneous anisotropic diffusion in generic three-dimensional topologies
AU - Kara, Erdi
AU - Rahman, Aminur
AU - Aulisa, Eugenio
AU - Ghosh, Souparno
N1 - Funding Information:
E.K. and E.A. are grateful to NSF (Grant No. DMS-1912902) for partial support of their contributions to this investigation. E.K., A.R., E.A., and S.G. appreciate the support of the Department of Mathematics and Statistics at TTU, S.G. also appreciates the support of the Department of Statistics at UNL, and A.R. appreciates the support of the Department of Applied Mathematics at UW. Finally, we would like to give our heartfelt thanks to the referees for their detailed suggestions, which played an integral part in improving this manuscript.
Publisher Copyright:
© 2020 American Physical Society.
PY - 2020/12/30
Y1 - 2020/12/30
N2 - In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.
AB - In recent decades computer-aided technologies have become prevalent in medicine, however, cancer drugs are often only tested on in vitro cell lines from biopsies. We derive a full three-dimensional model of inhomogeneous -anisotropic diffusion in a tumor region coupled to a binary population model, which simulates in vivo scenarios faster than traditional cell-line tests. The diffusion tensors are acquired using diffusion tensor magnetic resonance imaging from a patient diagnosed with glioblastoma multiform. Then we numerically simulate the full model with finite element methods and produce drug concentration heat maps, apoptosis hotspots, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.
UR - http://www.scopus.com/inward/record.url?scp=85099139245&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.102.062425
DO - 10.1103/PhysRevE.102.062425
M3 - Article
C2 - 33466110
AN - SCOPUS:85099139245
VL - 102
JO - Physical Review E
JF - Physical Review E
SN - 2470-0045
IS - 6
M1 - 062425
ER -