
Study of the Radiotherapy Treatment Margins in Prostate Cancer with Fuzzy Logic Model
Author(s) -
Santosh Kumar Patnaikuni,
Sapan Mohan Saini,
Rakesh Mohan Chandola,
Vivek Choudhary
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/798/1/012042
Subject(s) - radiation therapy , radiation treatment planning , margin (machine learning) , fuzzy logic , prostate cancer , convolution (computer science) , computer science , medicine , nuclear medicine , mathematics , cancer , radiology , artificial intelligence , machine learning , artificial neural network
Start your abstract here…External beam radiation therapy (EBRT) is a process in which therapeutic radiation dose is delivered to targettissues whilst seeking the risk of healthynormal and critical organs is minimal. In case of Complex radiation therapy technique such as volumetric modulated arc radiotherapy (VMAT), there is requirement of precise and optimal treatment margin selection so that not only the resulting dose distributions gives high tumor control probability(TCP) but also dose close to critical organs will get minimal. A fuzzy logic application was used here to derive the optimal radiotherapy treatment margins to be used in radiation therapy treatment planning. Here major all possible radiotherapy uncertainties accounted such as translational set-up, organdelineation and organ motion-induced errors for calculating the quantitative variations in target volume radiobiological parameters for treatment plans using fixed step sized increments of treatment margins up to maximum possible variations of tumor volumes. Therules and membership functions were adopted from dosimetricresults of treatment planning for the fuzzy inference system. The imprecision and smoothness of the original fuzzy output was corrected with application of a convolution technique. The results demonstrate that performance of the applied fuzzy model margins against current used radiotherapy margins was consistent inlower range of error magnitude but beyond that a significant non-linearly margin performance was found when considering the normal tissue complication probability (NTCP) and constraint factors into account in the margin recipe formulation. With the new margins obtained then applied for the study of prostate cancer treatment planning and results were compared well with current volumetric modulated arc radiotherapy (VMAT)technique.