improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment
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Improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment. Bongile Mzenda, Alexander Gegov, David Brown. Overview. Margins in radiotherapy Fuzzy networks Methodology Results Conclusions. Margins in radiotherapy. Account for presence of organ motion, - PowerPoint PPT PresentationTRANSCRIPT
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Improving the transparency in fuzzy modelling of radiotherapy
margins in cancer treatment
Bongile Mzenda, Alexander Gegov, David Brown
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Overview
• Margins in radiotherapy• Fuzzy networks• Methodology• Results• Conclusions
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Margins in radiotherapy
• Account for presence of organ motion, patient setup and delineation errors
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Margins methods
Shortcomings of presently used margin derivations methods:
•Do not include delineation errors
•Do not consider dose effects on surrounding critical organs
•Cannot be adapted to changing patientconditions
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Fuzzy networks
•Offer novel methodology to address above shortcomings
•Consist of networked rule based systems
•Deal with process inputs sequentially while taking into account the interactions and the structure of the system
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Fuzzy networks
General structure
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Methodology•Treatment study used to deduce variation in tumour and critical organ dose sensitive parameters (V99% & V60) with errors
•Fuzzy network model design
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MethodologyGaussian membership functions used for inputs and output
Linguistic composition of individual rule bases
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ResultsComparison to fuzzy system & Stroom et al statistical method
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ResultsComparison to fuzzy system & van Herk et al statistical method
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ResultsMean absolute error (MAE) analysis
Transparency index (TI)TI
Fuzzy network Fuzzy system1.25 4.00
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Conclusions
•Use of fuzzy network resulted in better correlation of input and output parameters
•Fuzzy network results lie in between currently used statistical methods
•Improved transparency from fuzzy network
•User friendly for clinical users to present their expert knowledge in rule design