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Multi-objective Optimization of Cancer Chemotherapy Treatment

  • Ewa Szlachcic
  • Pawel Porombka
  • Jerzy Kotowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6928)

Abstract

In cancer chemotherapy a tumor development is delivered by toxic drugs and the influence of drugs’ on human body is also checked. The minimization of the tumor burden at a fixed period of time and the minimization of the toxicity of drug regimes will be sought corresponding to the mathematical Goempertz growth model. The effective treatment schedules are search with the help of Modified Differential Evolution Multi-Objective Algorithm. The numerical tests of proposed algoritm show the outcomes for each drug regimes. The tumor is eradicated through the treatment period and the toxic effects are small enough according to the strong set of constraints. The simulation of human body answering for different treatment scenarios can help to an oncologist, which can explore treatment schedules before deciding upon admissible doses regimes for suitable clinical treatment.

Keywords

White Blood Cell Count Treatment Scenario Nondominated Sorting Nondominated Point Optimal Dose Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ewa Szlachcic
    • 1
  • Pawel Porombka
    • 1
  • Jerzy Kotowski
    • 1
  1. 1.Institute of Computer Engineering, Control and RoboticsWrocław University of TechnologyWrocławPoland

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