Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through and maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed and maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the map using the map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from map to map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand map to map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.