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Improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment

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,

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Improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment

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  1. Improving the transparency in fuzzy modelling of radiotherapy margins in cancer treatment Bongile Mzenda, Alexander Gegov, David Brown

  2. Overview • Margins in radiotherapy • Fuzzy networks • Methodology • Results • Conclusions

  3. Margins in radiotherapy • Account for presence of organ motion, • patient setup and delineation errors

  4. 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 patient • conditions

  5. 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

  6. Fuzzy networks General structure

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

  8. Methodology Gaussian membership functions used for inputs and output Linguistic composition of individual rule bases

  9. Results Comparison to fuzzy system & Stroom et al statistical method

  10. Results Comparison to fuzzy system & van Herk et al statistical method

  11. Results Mean absolute error (MAE) analysis Transparency index (TI)

  12. 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

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