1. Bongile Mzenda, Alexander Gegov, David Brown
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) TI
Fuzzy network
Fuzzy system
1.25
4.00

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