TY - JOUR
TI - Clinical implications of in silico mathematical modeling for glioblastoma: a critical review
AU - Protopapa, M.
AU - Zygogianni, A.
AU - Stamatakos, G.S.
AU - Antypas, C.
AU - Armpilia, C.
AU - Uzunoglu, N.K.
AU - Kouloulias, V.
JO - Journal of Neuro-Oncology
PY - 2018
VL - 136
TODO - 1
SP - null
PB - Springer New York LLC
SN - 0167-594X
TODO - 10.1007/s11060-017-2650-2
TODO - analytic method;  cancer growth;  cancer radiotherapy;  cancer research;  clinical practice;  clinical protocol;  computer analysis;  glioblastoma;  human;  mathematical model;  personalized medicine;  radiation response;  Review;  stochastic model;  three dimensional imaging;  biological model;  brain tumor;  computer simulation;  diagnostic imaging;  glioblastoma;  methodology;  pathophysiology;  theoretical model, Brain Neoplasms;  Computer Simulation;  Diagnostic Imaging;  Glioblastoma;  Humans;  Imaging, Three-Dimensional;  Models, Neurological;  Models, Theoretical;  Research Design
TODO - Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor’s point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments. © 2017, Springer Science+Business Media, LLC.
ER -