An evolutionary approach to cancer chemotherapy scheduling



Ochoa G, Villasana M & Burke E (2007) An evolutionary approach to cancer chemotherapy scheduling. Genetic Programming and Evolvable Machines, 8 (4), pp. 301-318.

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's 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.

evolutionary algorithms; evolution strategies; objective function; optimal control; cancer chemotherapy; cancer model; cycle-phase-specific drugs

Genetic Programming and Evolvable Machines: Volume 8, Issue 4

Publication date31/12/2007

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Professor Gabriela Ochoa

Professor Gabriela Ochoa

Professor, Computing Science