Models of Tumor Growth

Corina Drapaca, Siv Sivaloganathan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationFields Institute Monographs
PublisherSpringer New York LLC
Pages127-151
Number of pages25
DOIs
StatePublished - Jan 1 2019

Publication series

NameFields Institute Monographs
Volume37
ISSN (Print)1069-5273
ISSN (Electronic)2194-3079

Fingerprint

Brain Tumor
Tumor Growth
Histology
Soft Tissue
Invasion
Grading
Tumor
Health
Predict
Modeling
Model
Brain
Review

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

Cite this

Drapaca, C., & Sivaloganathan, S. (2019). Models of Tumor Growth. In Fields Institute Monographs (pp. 127-151). (Fields Institute Monographs; Vol. 37). Springer New York LLC. https://doi.org/10.1007/978-1-4939-9810-4_5
Drapaca, Corina ; Sivaloganathan, Siv. / Models of Tumor Growth. Fields Institute Monographs. Springer New York LLC, 2019. pp. 127-151 (Fields Institute Monographs).
@inbook{824c497420a94f589c4f9f728a511910,
title = "Models of Tumor Growth",
abstract = "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.",
author = "Corina Drapaca and Siv Sivaloganathan",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-1-4939-9810-4_5",
language = "English (US)",
series = "Fields Institute Monographs",
publisher = "Springer New York LLC",
pages = "127--151",
booktitle = "Fields Institute Monographs",

}

Drapaca, C & Sivaloganathan, S 2019, Models of Tumor Growth. in Fields Institute Monographs. Fields Institute Monographs, vol. 37, Springer New York LLC, pp. 127-151. https://doi.org/10.1007/978-1-4939-9810-4_5

Models of Tumor Growth. / Drapaca, Corina; Sivaloganathan, Siv.

Fields Institute Monographs. Springer New York LLC, 2019. p. 127-151 (Fields Institute Monographs; Vol. 37).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Models of Tumor Growth

AU - Drapaca, Corina

AU - Sivaloganathan, Siv

PY - 2019/1/1

Y1 - 2019/1/1

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

UR - http://www.scopus.com/inward/citedby.url?scp=85072797220&partnerID=8YFLogxK

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 -

Drapaca C, Sivaloganathan S. Models of Tumor Growth. In Fields Institute Monographs. Springer New York LLC. 2019. p. 127-151. (Fields Institute Monographs). https://doi.org/10.1007/978-1-4939-9810-4_5