Multiparameter computational modeling of tumor invasion.
Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability to quantify the connection between variables governing growth, prognosis, and treatment outcome. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis.
Bearer
Elaine L
EL
Department of Pathology and Laboratory Medicine, and Division of Engineering, Brown University, Providence, RI, USA.
Lowengrub
John S
JS
Frieboes
Hermann B
HB
Chuang
Yao-Li
YL
Jin
Fang
F
Wise
Steven M
SM
Ferrari
Mauro
M
Agus
David B
DB
Cristini
Vittorio
V
D001344:Autopsy; D001932:Brain Neoplasms / Q000098:blood supply / Q000473:pathology*; D002455:Cell Division; D002465:Cell Movement; D002470:Cell Survival; D003198:Computer Simulation; D005909:Glioblastoma / Q000098:blood supply / Q000473:pathology*; D006801:Humans; D007700:Kinetics; D008433:Mathematics; D008954:Models, Biological; D008962:Models, Theoretical; D009361:Neoplasm Invasiveness; D009369:Neoplasms / Q000473:pathology* / Q000628:therapy; D015203:Reproducibility of Results