Literature DB >> 10704301

A dynamical model for the growth and size distribution of multiple metastatic tumors.

K Iwata1, K Kawasaki, N Shigesada.   

Abstract

Metastasis is the spread of tumors culminating in the establishment of one or more secondary tumors at remote sites. In deciding the best treatment for cancer therapy, estimations of the colony size of metastatic tumors and predictions of the future spread of colonies are needed. A dynamical model for the colony size distribution of multiple metastatic tumors is presented here. The dynamics is described by equations that incorporate both the colonization by metastasis and the growth of each colony. When the colony growth is subject to the Gompertz function, the explicit solution obtained tends to an asymptotic stable distribution that shows a monotonically decreasing or U-shaped pattern according to the values of clinically significant parameters, such as the colonization coefficient and the fractal dimension of blood vessels. This predicted colony size distribution agrees well with successive data of a clinically observed size distribution of multiple metastatic tumors of liver cancer. The combined analysis of the theoretical colony size distribution and clinical data will give useful information on the diagnosis and the therapy for cancer patients. Copyright 2000 Academic Press.

Entities:  

Mesh:

Year:  2000        PMID: 10704301     DOI: 10.1006/jtbi.2000.1075

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  29 in total

1.  Parameter non-identifiability of the Gyllenberg-Webb ODE model.

Authors:  Niklas Hartung
Journal:  J Math Biol       Date:  2013-08-30       Impact factor: 2.259

2.  Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.

Authors:  Sebastien Benzekry; Amanda Tracz; Michalis Mastri; Ryan Corbelli; Dominique Barbolosi; John M L Ebos
Journal:  Cancer Res       Date:  2015-10-28       Impact factor: 12.701

3.  Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model.

Authors:  Paul K Newton; Jeremy Mason; Kelly Bethel; Lyudmila Bazhenova; Jorge Nieva; Larry Norton; Peter Kuhn
Journal:  Cancer Res       Date:  2013-02-27       Impact factor: 12.701

4.  Stochastic model of the formation of cancer metastases via cancer stem cells.

Authors:  Vladimir P Zhdanov
Journal:  Eur Biophys J       Date:  2008-05-08       Impact factor: 1.733

5.  Disease progression model of 4T1 metastatic breast cancer.

Authors:  Liang Yang; Ling Yong; Xiao Zhu; Yaoyao Feng; Yu Fu; Daming Kong; Wei Lu; Tian-Yan Zhou
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-01-22       Impact factor: 2.745

6.  A new method to estimate parameters of the growth model for metastatic tumours.

Authors:  Esmaeil Mehrara; Eva Forssell-Aronsson; Viktor Johanson; Lars Kölby; Ragnar Hultborn; Peter Bernhardt
Journal:  Theor Biol Med Model       Date:  2013-05-09       Impact factor: 2.432

7.  Are metastases from metastases clinical relevant? Computer modelling of cancer spread in a case of hepatocellular carcinoma.

Authors:  Anja Bethge; Udo Schumacher; Andreas Wree; Gero Wedemann
Journal:  PLoS One       Date:  2012-04-23       Impact factor: 3.240

8.  A dynamic model for tumour growth and metastasis formation.

Authors:  Volker Haustein; Udo Schumacher
Journal:  J Clin Bioinforma       Date:  2012-07-05

9.  How mathematical modeling could contribute to the quantification of metastatic tumor burden under therapy: insights in immunotherapeutic treatment of non-small cell lung cancer.

Authors:  Pirmin Schlicke; Christina Kuttler; Christian Schumann
Journal:  Theor Biol Med Model       Date:  2021-06-02       Impact factor: 2.432

Review 10.  Computational systems biology in cancer brain metastasis.

Authors:  Huiming Peng; Hua Tan; Weiling Zhao; Guangxu Jin; Sambad Sharma; Fei Xing; Kounosuke Watabe; Xiaobo Zhou
Journal:  Front Biosci (Schol Ed)       Date:  2016-01-01
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.