Background
The [18F]-fluoroethyl-L-tyrosine (FET) PET in Glioblastoma (FIG) Study is an Australian, prospective, multicenter trial evaluating the impact of FET PET on the treatment management of glioblastoma patients. Up to 210 adult glioblastoma patients will undergo FET PET pre-radiochemotherapy, one-month post-radiochemotherapy, and at suspected progression. Detailed analysis of static and dynamic FET PET images and comparison to clinical outcomes is a key objective of the trial, and computational modelling approaches are part of this approach.
Aims
To develop an automatic FET PET segmentation network to enhance computational analysis of the FIG study image datasets.
Methods
Although we have experience in quantitative analysis of FET PET, to understand the current framework of artificial intelligence approaches, we have also conducted a review of the literature to gauge its applicability to the FIG study. The methodologies employed in these studies will be incorporated into network and model design, highlighting what tools may be developed, to create efficient workflows to analyse the resulting trial data.
Results
Development of a multilabel automatic segmentation solution for FET PET images will be presented, taking advantage of state-of-the-art, intuitive networks. Our approach will conform with published guidelines when contouring a biological tumour volume, noting the emerging relevance of neural networks to final workflows. The automatically generated structures may then be used to assess treatment response by change in quantitative metrics. In addition, radiomics and AI models can follow once a network is established, which we will explore in the FIG study, using static and dynamic image data.
Conclusion
The data obtained from the ongoing FIG trial will present a unique opportunity to use computational analysis and artificial intelligence to develop automatic segmentation networks and prognostic models for FET PET studies.