Deriving margins in prostate cancer radiotherapy treatment: comparison of neural network and fuzzy logic models Online publication date: Fri, 05-Dec-2014
by Bongile Mzenda; Alexander Gegov; David J. Brown; Nedyalko Petrov
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 8, No. 5/6, 2012
Abstract: This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.
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