Literature DB >> 25081547

A comparison and catalog of intrinsic tumor growth models.

E A Sarapata1, L G de Pillis.   

Abstract

Determining the mathematical dynamics and associated parameter values that should be used to accurately reflect tumor growth continues to be of interest to mathematical modelers, experimentalists and practitioners. However, while there are several competing canonical tumor growth models that are often implemented, how to determine which of the models should be used for which tumor types remains an open question. In this work, we determine the best fit growth dynamics and associated parameter ranges for ten different tumor types by fitting growth functions to at least five sets of published experimental growth data per type of tumor. These time-series tumor growth data are used to determine which of the five most common tumor growth models (exponential, power law, logistic, Gompertz, or von Bertalanffy) provides the best fit for each type of tumor.

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Year:  2014        PMID: 25081547     DOI: 10.1007/s11538-014-9986-y

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  21 in total

1.  Mathematical models incorporating a multi-stage cell cycle replicate normally-hidden inherent synchronization in cell proliferation.

Authors:  Sean T Vittadello; Scott W McCue; Gency Gunasingh; Nikolas K Haass; Matthew J Simpson
Journal:  J R Soc Interface       Date:  2019-08-21       Impact factor: 4.118

2.  Estimating Tumor Growth Rates In Vivo.

Authors:  Anne Talkington; Rick Durrett
Journal:  Bull Math Biol       Date:  2015-10       Impact factor: 1.758

3.  Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies.

Authors:  Aaron N Hata; Harald Paganetti; Clemens Grassberger; David McClatchy; Changran Geng; Sophia C Kamran; Florian Fintelmann; Yosef E Maruvka; Zofia Piotrowska; Henning Willers; Lecia V Sequist
Journal:  Cancer Res       Date:  2019-05-21       Impact factor: 12.701

4.  Predicting population extinction in lattice-based birth-death-movement models.

Authors:  Stuart T Johnston; Matthew J Simpson; Edmund J Crampin
Journal:  Proc Math Phys Eng Sci       Date:  2020-06-03       Impact factor: 2.704

5.  Optimal Quantification of Contact Inhibition in Cell Populations.

Authors:  David J Warne; Ruth E Baker; Matthew J Simpson
Journal:  Biophys J       Date:  2017-10-13       Impact factor: 4.033

6.  Identifying density-dependent interactions in collective cell behaviour.

Authors:  Alexander P Browning; Wang Jin; Michael J Plank; Matthew J Simpson
Journal:  J R Soc Interface       Date:  2020-04-29       Impact factor: 4.118

7.  Differences in predictions of ODE models of tumor growth: a cautionary example.

Authors:  Hope Murphy; Hana Jaafari; Hana M Dobrovolny
Journal:  BMC Cancer       Date:  2016-02-26       Impact factor: 4.430

8.  The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments.

Authors:  Bertin Hoffmann; Tobias Lange; Vera Labitzky; Kristoffer Riecken; Andreas Wree; Udo Schumacher; Gero Wedemann
Journal:  BMC Cancer       Date:  2020-06-05       Impact factor: 4.430

9.  QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

Authors:  Richard J Sové; Mohammad Jafarnejad; Chen Zhao; Hanwen Wang; Huilin Ma; Aleksander S Popel
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-09-07

10.  Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections.

Authors:  Jana L Gevertz; Joanna R Wares
Journal:  Comput Math Methods Med       Date:  2018-10-30       Impact factor: 2.238

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