Computational intelligence margin models for radiotherapeutic cancer treatment

The derivation of margins for use in external beam radiotherapy involves a complex balance between ensuring adequate tumour dose coverage that will lead to cure of the cancer whilst sufficiently sparing the surrounding organs at risk (OARs). The treatment of cancer using ionising radiation is currently witnessing unprecedented levels of new treatment techniques and equipment being introduced. These new treatment strategies, with improved imaging during treatment, are aimed at improved radiation dose conformity to dynamic targets and better sparing of the healthy tissues. However, with the adoption of these new techniques for radiotherapy, the validity of the continued use of recommended statistical model based margin formulations to calculate the treatment margins is now being questioned more than ever before. To derive margins for use in treatment planning which address present shortcomings, this study utilised novel applications of fuzzy logic and neural network techniques to the PTV margin problem. As an extension of this work a new hybrid fuzzy network technique was also adopted for use in margin derivation, a novel application of this technique which required new rule formulations and rule base manipulations. The new margin models developed in this study utilised a novel combination of the radiotherapy errors and their radiobiological effects which was previously difficult to establish using mathematical methods. This was achieved using fuzzy rules and neural network input layers. An advantage of the neural network procedure was that fewer computational steps were needed to calculate the final result whereas the fuzzy based techniques required a significant number of iterative computational steps including the definition of the fuzzy rules and membership functions prior to computation of the final result. An advantage of the fuzzy techniques was their ability to use fewer data points to deduce the relationship between the output and input parameters. In contrast the neural network model requires a large amount of training data. The previously stated limitations of currently recommended statistical techniques were addressed by application of the fuzzy and neural network models. A major advantage of the computational intelligence methods from this study is that they allow the calculation of patient-specific margins. Radiotherapy planning currently relies on the use of ‘one size fits all’ class solutions for margins for each tumour site and with the large variability in patient physiology these margins may not be suitable for use in some cases. The models from this study can be applied to other treatment sites, including brain, lung and gastric tumours.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Handling organ motion in radiotherapy of cancer via Markov chains

This study developed a patient specific mathematical model holding a high potential to cope with patient setup error, internal and physiological organ motion in radiotherapy treatment. The proposed recipe falls into two stages; determining the stationary probability distribution of tumor motion in Markovian sense and then forwarding it as input to covering tumor process in an optimal frame by the knapsack problem, which is well known in operations research. The model is constructed in terms of tumor area, partitioned into small cells, instead of length. The proposed model allows clinicians to add arbitrary extra safety area as corresponding to CTV-to-PTV margin. The method is implemented for a hypothetical prostate cancer and solved by MS Excel. lt is undoubtedly needed serious and controlled clinical observations to validate the theoretical model, which is an application of operations research to medicine. (C) 2006 Elsevier Inc. All rights reserved