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, patient setup and delineation errors

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

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

Fuzzy networks

General structure

Methodology•Treatment study used to deduce variation in tumour and critical organ dose sensitive parameters (V99% & V60) with errors

•Fuzzy network model design

MethodologyGaussian membership functions used for inputs and output

Linguistic composition of individual rule bases

ResultsComparison to fuzzy system & Stroom et al statistical method

ResultsComparison to fuzzy system & van Herk et al statistical method

ResultsMean absolute error (MAE) analysis

Transparency index (TI)TI

Fuzzy network Fuzzy system1.25 4.00

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