In this paper, we investigate the employment of
evolutionary algorithms as a search mechanism in a
decision support system for designing chemotherapy
schedules. Chemotherapy involves using powerful
anti-cancer drugs to help eliminate cancerous cells and
cure the condition. It is given in cycles of treatment
alternating with rest periods to allow the body to
recover from toxic side-effects. The number and
duration of these cycles would depend on many factors,
and the oncologist would schedule a treatment for each
patient's condition. The design of a chemotherapy
schedule can be formulated as an optimal control
problem; using an underlying mathematical model of
tumour growth (that considers interactions with the
immune system and multiple applications of a
cycle-phase-specific drug), the objective is to find
effective drug schedules that help eradicate the tumour
while maintaining the patient health above an
acceptable level. A detailed study on the effects of
different objective functions, in the quality and
diversity of the solutions, was performed. A term that
keeps at a minimum the tumour levels throughout the
course of treatment was found to produce more regular
treatments, at the expense of imposing a higher strain
on the patient's health, and reducing the diversity of
the solutions. Moreover, when the number of cycles was
incorporated in the problem encoding, and a parsimony
pressure added to the objective function, shorter
treatments were obtained than those initially found by
trial and error.
%0 Journal Article
%1 Ochoa:2007:GPEM
%A Ochoa, Gabriela
%A Villasana, Minaya
%A Burke, Edmund K.
%D 2007
%J Genetic Programming and Evolvable Machines
%K CMA-ES, Cancer Cycle-phase-specific Evolution Evolutionary Objective Optimal algorithms, annealing, chemotherapy, control, drugs function, genetic model, simulated strategies,
%N 4
%P 301--318
%R doi:10.1007/s10710-007-9041-y
%T An evolutionary approach to cancer chemotherapy
scheduling
%V 8
%X In this paper, we investigate the employment of
evolutionary algorithms as a search mechanism in a
decision support system for designing chemotherapy
schedules. Chemotherapy involves using powerful
anti-cancer drugs to help eliminate cancerous cells and
cure the condition. It is given in cycles of treatment
alternating with rest periods to allow the body to
recover from toxic side-effects. The number and
duration of these cycles would depend on many factors,
and the oncologist would schedule a treatment for each
patient's condition. The design of a chemotherapy
schedule can be formulated as an optimal control
problem; using an underlying mathematical model of
tumour growth (that considers interactions with the
immune system and multiple applications of a
cycle-phase-specific drug), the objective is to find
effective drug schedules that help eradicate the tumour
while maintaining the patient health above an
acceptable level. A detailed study on the effects of
different objective functions, in the quality and
diversity of the solutions, was performed. A term that
keeps at a minimum the tumour levels throughout the
course of treatment was found to produce more regular
treatments, at the expense of imposing a higher strain
on the patient's health, and reducing the diversity of
the solutions. Moreover, when the number of cycles was
incorporated in the problem encoding, and a parsimony
pressure added to the objective function, shorter
treatments were obtained than those initially found by
trial and error.
@article{Ochoa:2007:GPEM,
abstract = {In this paper, we investigate the employment of
evolutionary algorithms as a search mechanism in a
decision support system for designing chemotherapy
schedules. Chemotherapy involves using powerful
anti-cancer drugs to help eliminate cancerous cells and
cure the condition. It is given in cycles of treatment
alternating with rest periods to allow the body to
recover from toxic side-effects. The number and
duration of these cycles would depend on many factors,
and the oncologist would schedule a treatment for each
patient's condition. The design of a chemotherapy
schedule can be formulated as an optimal control
problem; using an underlying mathematical model of
tumour growth (that considers interactions with the
immune system and multiple applications of a
cycle-phase-specific drug), the objective is to find
effective drug schedules that help eradicate the tumour
while maintaining the patient health above an
acceptable level. A detailed study on the effects of
different objective functions, in the quality and
diversity of the solutions, was performed. A term that
keeps at a minimum the tumour levels throughout the
course of treatment was found to produce more regular
treatments, at the expense of imposing a higher strain
on the patient's health, and reducing the diversity of
the solutions. Moreover, when the number of cycles was
incorporated in the problem encoding, and a parsimony
pressure added to the objective function, shorter
treatments were obtained than those initially found by
trial and error.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Ochoa, Gabriela and Villasana, Minaya and Burke, Edmund K.},
biburl = {https://www.bibsonomy.org/bibtex/25deb96160f2995cde537eb974f2573f0/brazovayeye},
doi = {doi:10.1007/s10710-007-9041-y},
interhash = {f146b38a084cb03806df9479a600b385},
intrahash = {5deb96160f2995cde537eb974f2573f0},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {CMA-ES, Cancer Cycle-phase-specific Evolution Evolutionary Objective Optimal algorithms, annealing, chemotherapy, control, drugs function, genetic model, simulated strategies,},
month = {December},
note = {special issue on medical applications of Genetic and
Evolutionary Computation},
notes = {Matlab, ROC AUC, ISH},
number = 4,
pages = {301--318},
timestamp = {2008-06-19T17:48:43.000+0200},
title = {An evolutionary approach to cancer chemotherapy
scheduling},
volume = 8,
year = 2007
}