TY - CHAP
T1 - Models of Tumor Growth
AU - Drapaca, Corina
AU - Sivaloganathan, Siv
N1 - Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019
Y1 - 2019
N2 - In 2016 the World Health Organization provided the most recent classification of tumors of the central nervous system based on histology, molecular mechanisms, rate of brain invasion, and a soft tissue-type grading system. The classification of various benign and malignant brain tumors can be used as well as improved by the integration of in silico, in vitro and in vivo studies of brain tumors that will ultimately lead to better diagnosis, treatment protocols and outcomes. In this chapter we review some of the modeling approaches proposed in the literature to predict tumor growth and therapeutic outcome.
AB - In 2016 the World Health Organization provided the most recent classification of tumors of the central nervous system based on histology, molecular mechanisms, rate of brain invasion, and a soft tissue-type grading system. The classification of various benign and malignant brain tumors can be used as well as improved by the integration of in silico, in vitro and in vivo studies of brain tumors that will ultimately lead to better diagnosis, treatment protocols and outcomes. In this chapter we review some of the modeling approaches proposed in the literature to predict tumor growth and therapeutic outcome.
UR - http://www.scopus.com/inward/record.url?scp=85072797220&partnerID=8YFLogxK
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U2 - 10.1007/978-1-4939-9810-4_5
DO - 10.1007/978-1-4939-9810-4_5
M3 - Chapter
AN - SCOPUS:85072797220
T3 - Fields Institute Monographs
SP - 127
EP - 151
BT - Fields Institute Monographs
PB - Springer New York LLC
ER -