Abstract
Radiotherapy is an effective treatment for many cancer patients. The target volume to be irradiated is defined by the radiotherapist. In order to cure a patient, the target volume must comprise all potential tumor cells. However, the imaging techniques on which the process of target volume determination is based can visualize the gross tumor mass but not individual cells. This causes diagnostic uncertainties, leading to a fuzziness of the target volume. In order to assist the radiotherapist in the definition of the optimal target volume for an individual patient, a new approach based on Fuzzy Logic was developed that incorporates the fuzziness in the target volume definition: The radiotherapist defines a minimal target volume which surely contains tumor cells and a maximal target volume, outside of which no tumor cell spread is expected. The fuzziness region in between is processed by a knowledge-based fuzzy system. This system contains fuzzy sets and rules laid down by experienced radiotherapists. It is used to calculate the membership values of individual subvolumes in the fuzziness region with respect to the target volume. The result is a fuzzy target volume. An application of the subset defuzzification yields a crisp target volume with optimized extension which is proposed to the radiotherapist. The new approach was applied to test phantoms and clinical case examples.
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