INVERSE RADIATION THERAPY PLANNING: A MULTIPLE OBJECTIVE OPTIMISATION APPROACH

H. W. Hamacher and K. H. Küfer
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H. W. Hamacher: Institute for Techno- and Econo-Mathematics (ITWM) and Department of Mathematics, University of Kaiserslautern, Kaiserslautern, Germany
K. H. Küfer: Institute for Techno- and Econo-Mathematics (ITWM) and Department of Mathematics, University of Kaiserslautern, Kaiserslautern, Germany

Chapter 16 in Monitoring, Evaluating, Planning Health Services, 1999, pp 177-189 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractFor some decades radiation therapy - external beam radiation therapy as well as brachytherapy - has been proved successful in cancer treatment. It is the major task of clinical radiation treatment planning to realise on the one hand a high level dose of radiation in the cancer tissue in order to obtain maximum tumour control. On the other hand it is obvious that it is absolutely necessary to keep in the tissue outside the tumour, particularly in organs at risk, the unavoidable radiation as low as possible. No doubt, these two objectives of treatment planning – high level dose in the tumour, low radiation outside the tumour – have a basically contradictory nature. Therefore, it is no surprise that inverse mathematical models with prescribed dose distributions tend to be infeasible in most cases. Thus, there is need for approximations compromising between overdosing the organs at risk and underdosing the target volume. Differing from the currently used time consuming iterative approach, which measures deviation from an ideal (non-achievable) treatment plan using recursively trial-and-error weights for the organs of interest, we go a new way trying to avoid a priori weight choices and consider the treatment planning problem as a multiple objective linear programming problem: with each organ of interest, target tissue as well as organs at risk, we associate an objective function measuring the maximal deviation from the prescribed doses. We build up a data base of relatively few efficient solutions representing and approximating the variety of Pareto solutions of the multiple objective linear programming problem. This data base can be easily scanned by physicians looking for an adequate treatment plan with the aid of an appropriate online tool.

Keywords: Healthcare; Management; Quality; Planning; Emergency Services; Evaluation; Hospital Systems; Monitoring (search for similar items in EconPapers)
Date: 1999
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