title: A Markov decision process approach to temporal modulation of dose fractions in radiation therapy planning.
abstract: The current state of the art in cancer treatment by radiation optimizes beam intensity spatially such that tumors receive high dose radiation whereas damage to nearby healthy tissues is minimized. It is common practice to deliver the radiation over several weeks, where the daily dose is a small constant fraction of the total planned. Such a 'fractionation schedule' is based on traditional models of radiobiological response where normal tissue cells possess the ability to repair sublethal damage done by radiation. This capability is significantly less prominent in tumors. Recent advances in quantitative functional imaging and biological markers are providing new opportunities to measure patient response to radiation over the treatment course. This opens the door for designing fractionation schedules that take into account the patient's cumulative response to radiation up to a particular treatment day in determining the fraction on that day. We propose a novel approach that, for the first time, mathematically explores the benefits of such fractionation schemes. This is achieved by building a stylistic Markov decision process (MDP) model, which incorporates some key features of the problem through intuitive choices of state and action spaces, as well as transition probability and reward functions. The structure of optimal policies for this MDP model is explored through several simple numerical examples.
authors: Department of Applied Mathematics, University of Washington, Seattle, WA, USA. mk688@u.washington.edu
authors: M
authors: M
authors: Kim
authors: A
authors: A
authors: Ghate
authors: M H
authors: MH
authors: Phillips
mesh_terms: D003198:Computer Simulation; D003661:Decision Support Techniques; D019583:Dose Fractionation, Radiation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009369:Neoplasms / Q000532:radiotherapy*; D011874:Radiometry / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*