Literature DB >> 23393201

The model muddle: in search of tumor growth laws.

Philip Gerlee1.   

Abstract

In this article, we will trace the historical development of tumor growth laws, which in a quantitative fashion describe the increase in tumor mass/volume over time. These models are usually formulated in terms of differential equations that relate the growth rate of the tumor to its current state and range from the simple one-parameter exponential growth model to more advanced models that contain a large number of parameters. Understanding the assumptions and consequences of such models is important, as they often underpin more complex models of tumor growth. The conclusion of this brief survey is that although much improvement has occurred over the last century, more effort and new models are required if we are to understand the intricacies of tumor growth. ©2013 AACR.

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Year:  2013        PMID: 23393201     DOI: 10.1158/0008-5472.CAN-12-4355

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  65 in total

1.  Akt activation by Ca2+/calmodulin-dependent protein kinase kinase 2 (CaMKK2) in ovarian cancer cells.

Authors:  Angela M Gocher; Gissou Azabdaftari; Lindsey M Euscher; Shuhang Dai; Loukia G Karacosta; Thomas F Franke; Arthur M Edelman
Journal:  J Biol Chem       Date:  2017-06-20       Impact factor: 5.157

2.  Bridging population and tissue scale tumor dynamics: a new paradigm for understanding differences in tumor growth and metastatic disease.

Authors:  Sylvia Plevritis; Alexander R A Anderson; Jill Gallaher; Aravind Babu
Journal:  Cancer Res       Date:  2014-01-09       Impact factor: 12.701

3.  Mathematical Models for Tumor Growth and the Reduction of Overtreatment.

Authors:  Berdine L Heesterman; John-Melle Bokhorst; Lisa M H de Pont; Berit M Verbist; Jean-Pierre Bayley; Andel G L van der Mey; Eleonora P M Corssmit; Frederik J Hes; Peter Paul G van Benthem; Jeroen C Jansen
Journal:  J Neurol Surg B Skull Base       Date:  2018-07-23

4.  A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.

Authors:  Juan Jiménez-Sánchez; Álvaro Martínez-Rubio; Anton Popov; Julián Pérez-Beteta; Youness Azimzade; David Molina-García; Juan Belmonte-Beitia; Gabriel F Calvo; Víctor M Pérez-García
Journal:  PLoS Comput Biol       Date:  2021-02-10       Impact factor: 4.475

5.  Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses.

Authors:  Enakshi D Sunassee; Dean Tan; Nathan Ji; Renee Brady; Eduardo G Moros; Jimmy J Caudell; Slav Yartsev; Heiko Enderling
Journal:  Int J Radiat Biol       Date:  2019-03-19       Impact factor: 2.694

6.  Estimating Tumor Growth Rates In Vivo.

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

7.  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

8.  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

9.  Neuronal Activity Promotes Glioma Growth through Neuroligin-3 Secretion.

Authors:  Humsa S Venkatesh; Tessa B Johung; Viola Caretti; Alyssa Noll; Yujie Tang; Surya Nagaraja; Erin M Gibson; Christopher W Mount; Jai Polepalli; Siddhartha S Mitra; Pamelyn J Woo; Robert C Malenka; Hannes Vogel; Markus Bredel; Parag Mallick; Michelle Monje
Journal:  Cell       Date:  2015-04-23       Impact factor: 41.582

Review 10.  Computational oncology--mathematical modelling of drug regimens for precision medicine.

Authors:  Dominique Barbolosi; Joseph Ciccolini; Bruno Lacarelle; Fabrice Barlési; Nicolas André
Journal:  Nat Rev Clin Oncol       Date:  2015-11-24       Impact factor: 66.675

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