Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models

Tamas Ferenci, Johanna Sapi, L. Kovács

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Antiangiogenic inhibitors offer a promising new treatment modality in oncology. However, the optimal administration regime is often not well-established, despite the fact that it might have substantial impact on the outcome. The aim of the present study was to investigate this issue. Eight weeks old male C57Bl/6 mice were implanted with C38 colon adenocarcinoma, and were given either daily (n = 9) or single (n = 5) dose of bevacizumab; both receiving the same dose the only difference being the administration pattern. Outcome was measured by tracking tumor volume; both caliper and magnetic resonance imaging was employed. Longitudinal growth curves were modelled with mixed-effects models (with correction for autocorrelation and heteroscedasticity, where necessary) to infer on population-level. Several different growth models (exponential, logistic, Gompertz) were applied and compared. Results show that the estimation of the exponential model is very reliable, but it prevents extrapolation in time. Nevertheless, it clearly established the advantage of the continuous regime.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3912-3917
Number of pages6
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - Feb 6 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: Oct 9 2016Oct 12 2016

Other

Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period10/9/1610/12/16

Fingerprint

Mixed Effects Model
Tumor Growth
Tumors
Dose
Oncology
Growth Curve
Heteroscedasticity
Exponential Model
Magnetic Resonance Imaging
Growth Model
Autocorrelation
Extrapolation
Modeling
Modality
Logistics
Inhibitor
Mouse
Tumor
Magnetic resonance
Necessary

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

Cite this

Ferenci, T., Sapi, J., & Kovács, L. (2017). Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 3912-3917). [7844845] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844845

Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. / Ferenci, Tamas; Sapi, Johanna; Kovács, L.

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3912-3917 7844845.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ferenci, T, Sapi, J & Kovács, L 2017, Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844845, Institute of Electrical and Electronics Engineers Inc., pp. 3912-3917, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 10/9/16. https://doi.org/10.1109/SMC.2016.7844845
Ferenci T, Sapi J, Kovács L. Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3912-3917. 7844845 https://doi.org/10.1109/SMC.2016.7844845
Ferenci, Tamas ; Sapi, Johanna ; Kovács, L. / Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3912-3917
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