Sébastien Benzekry
INRIA Bordeaux
http://benzekry.perso.math.cnrs.fr/recherche.html
Date(s) : 15/03/2021 iCal
15 h 00 min - 16 h 00 min
In this talk, I will first introduce a new Inria-Inserm unit entitled COMPO (COMPutational pharmacology and clinical Oncology, Inria-Inserm, Center for Cancer Research of Marseille, France) which uniquely gathers clinical oncologists, pharmacists and mathematicians. The objective of the team is to develop novel mathematical constructs to model data arising from experimental and clinical oncology. Ultimately, the models are translated into numerical software of direct use in the clinic, either for the design of dosing regimen in clinical trials, or personalized medicine.
I will first give an example using mixed-effects modeling to describe and understand experimental tumor growth kinetics. We observed that the experimental and logistic growth models were unable to describe the data, in contrast with the Gompertz model. The population approach was then further leveraged to define a new model, the reduced Gompertz model, with only one individual-specific parameter (instead of two for the Gompertz) but still similarly good descriptive power. Such reduction in the number of parameters substantially improved the performance of the model when trying to predict the initiation time (inoculation) from late measurements. Improvements were particularly drastic when combining the population approach with Bayesian estimation.
If time allows, I will then present a concrete example of routine use of Bayesian estimation in the Marseille university hospital for adaptive and personalized dosing of cisplatin in head and neck cancer patients.
I will then conclude by presenting a few starting projects and the associated methodological challenges from a statistical learning point of view.
Reference:
Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors
C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. Mochel, M. Mastri, C. Poignard, J. ML Ebos, S. Benzekry
PLoS Computational Biology, Volume 16, Issue 2, e1007178, bioRxiv, 2020
I will first give an example using mixed-effects modeling to describe and understand experimental tumor growth kinetics. We observed that the experimental and logistic growth models were unable to describe the data, in contrast with the Gompertz model. The population approach was then further leveraged to define a new model, the reduced Gompertz model, with only one individual-specific parameter (instead of two for the Gompertz) but still similarly good descriptive power. Such reduction in the number of parameters substantially improved the performance of the model when trying to predict the initiation time (inoculation) from late measurements. Improvements were particularly drastic when combining the population approach with Bayesian estimation.
If time allows, I will then present a concrete example of routine use of Bayesian estimation in the Marseille university hospital for adaptive and personalized dosing of cisplatin in head and neck cancer patients.
I will then conclude by presenting a few starting projects and the associated methodological challenges from a statistical learning point of view.
Reference:
Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors
C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. Mochel, M. Mastri, C. Poignard, J. ML Ebos, S. Benzekry
PLoS Computational Biology, Volume 16, Issue 2, e1007178, bioRxiv, 2020
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