Literature DB >> 3690626

Growth in solid heterogeneous human colon adenocarcinomas: comparison of simple logistical models.

S Michelson1, A S Glicksman, J T Leith.   

Abstract

Three models of simple logistical growth were used to describe volumetric growth in heterogeneous tumours. Two clonal subpopulations (designated as clone A and clone D) originally obtained from a human colon adenocarcinoma were used to produce solid xenograft tumours in nude mice. Volumetric growth of tumours produced from pure cells alone was compared to that produced from 50% A:50% D, 88% A:12% D, and 9% A:91% D admixtures. Gompertzian analysis of the in vivo growth data indicated significant differences in both the initial growth rates and final asymptotic limiting volumes of the pure versus the admixed tumours. Verhulstian and modified Verhulstian models were also used to derive regression curves from the same data. The fit of the curves was compared with each other using standard (Akaike, 1974; Schwartz, 1978) information criteria. In four of the five tumour populations the Gompertz equation fitted best. Only in the 88% A:12% D tumours did the modified Verhulst model fit best. The deviations from the regression curves, the residuals, for all three models were systematically distributed. These systematic errors are likely to be the result of using simplified logistical models to describe the growth kinetics of interacting populations in heterogeneous tumours.

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Year:  1987        PMID: 3690626     DOI: 10.1111/j.1365-2184.1987.tb01316.x

Source DB:  PubMed          Journal:  Cell Tissue Kinet        ISSN: 0008-8730


  8 in total

1.  Tumor growth in vivo and as multicellular spheroids compared by mathematical models.

Authors:  M Marusić; Z Bajzer; S Vuk-Pavlović; J P Freyer
Journal:  Bull Math Biol       Date:  1994-07       Impact factor: 1.758

2.  Autocrine and paracrine growth factors in tumor growth: a mathematical model.

Authors:  S Michelson; J Leith
Journal:  Bull Math Biol       Date:  1991       Impact factor: 1.758

3.  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

4.  Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography.

Authors:  Feng Yan; Gokhan Gunay; Trisha I Valerio; Chen Wang; Jayla A Wilson; Majood S Haddad; Maegan Watson; Michael O Connell; Noah Davidson; Kar-Ming Fung; Handan Acar; Qinggong Tang
Journal:  Biomed Opt Express       Date:  2021-05-13       Impact factor: 3.732

5.  Competitive exclusion of clonal subpopulations in heterogeneous tumours after stromal injury.

Authors:  J T Leith; S Michelson; A S Glicksman
Journal:  Br J Cancer       Date:  1989-01       Impact factor: 7.640

6.  Classical mathematical models for description and prediction of experimental tumor growth.

Authors:  Sébastien Benzekry; Clare Lamont; Afshin Beheshti; Amanda Tracz; John M L Ebos; Lynn Hlatky; Philip Hahnfeldt
Journal:  PLoS Comput Biol       Date:  2014-08-28       Impact factor: 4.475

7.  Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis.

Authors:  Charalambos Loizides; Demetris Iacovides; Marios M Hadjiandreou; Gizem Rizki; Achilleas Achilleos; Katerina Strati; Georgios D Mitsis
Journal:  PLoS One       Date:  2015-12-09       Impact factor: 3.240

8.  Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors.

Authors:  Cristina Vaghi; Anne Rodallec; Raphaëlle Fanciullino; Joseph Ciccolini; Jonathan P Mochel; Michalis Mastri; Clair Poignard; John M L Ebos; Sébastien Benzekry
Journal:  PLoS Comput Biol       Date:  2020-02-25       Impact factor: 4.475

  8 in total

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