S2814
RTT - RTT education, training, and advanced practice
ESTRO 2026
Nathaniel Barry 15,9 , Sweet Ping Ng 16 , Mark B Pinkham 17,18 , Michael Back 19 , Nicholas Bucknell 8 , Eng- Siew Koh 14,20 1 Radiation Therapy Quality Assurance, Trans-Tasman Radiation Oncology Group (TROG) Cancer Research, Newcastle, Australia. 2 Tumour Targeting Program, Olivia Newton-John Cancer Research Institute, Heidelberg, Australia. 3 Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Australia. 4 Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, Australia. 5 Medical School, The University of Western Australia, Crawley, Australia. 6 Department of Radiology, Austin Health, Heidelberg, Australia. 7 Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia. 8 Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia. 9 School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Australia. 10 Department of Medical Physics, Liverpool Hospital, Liverpool, Australia. 11 Medical Physics Group, Ingham Institute, Liverpool, Australia. 12 MHF Centre for Brain Cancer Research, The University of Newcastle, Newcastle, Australia. 13 Department of Radiation Oncology, GenesisCare, Newcastle, Australia. 14 Department of Radiation Oncology, Liverpool Hospital, Liverpool, Australia. 15 Department of Radiation Oncology,, Sir Charles Gairdner Hospital, Perth, Australia. 16 Department of Radiation Oncology, Austin Health, Heidelberg, Australia. 17 Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia. 18 Department of Radiation Oncology, ICON Cancer Centre, Brisbane, Australia. 19 Department of Radiation Oncology, Royal North Shore Hospital, Sydney, Australia. 20 South West Sydney Clinical School, University of New South Wales, Sydney, Australia Purpose/Objective: To implement multi-modal imaging workflows as part of the ([18F]fluoroethyl)-L-tyrosine (FET)-PET in Glioblastoma (FIG) prospective phase 2 study (TROG 18.06) incorporating radiotherapy treatment planning CT, MRI and FET-PET. This workflow is a key requirement to enable radiation therapy (RT) target volume delineation and treatment planning comparisons. Material/Methods: The FIG trial is a multi-centre study across 11 credentialled Australian sites evaluating FET-PET in Glioblastoma management1. For Group 1 participants who undergo FET-PET1 pre-chemoradiation, the site Nuclear Medicine Physician (NMP) delineates a FET- PET defined biological target volume (BTV), using a custom workflow in MIM7.0 software. The BTV is then centrally reviewed by another NMP expert before the BTV and FET-PET imaging is provided to the site
radiation oncologist (RO). This process occurs after standard RT treatment planning incorporating MRI for contouring has been completed. A hybrid target volume is then delineated incorporating the BTV. An additional trial-specific workflow was developed to generate a CSV file to enable comparison of clinical versus hybrid volume metrics and indices to be extracted. After central review, the data is exported to undergo RT planning of hybrid volumes. For this, the FIG Adaptive Radiation Oncology (FIGARO) automated planning workflow has been created. Quality assurance radiation therapists (RTTs) perform data verification, workflow testing, and case preparation, ensuring accuracy and consistency prior to review. Results: Workflows have been successfully implemented across 11 trial sites, involving 21 NMP and over 25 ROs. The data from 143 study evaluable participants has undergone FET-PET1 and hybrid volume delineation followed by central NMP and RO review then hybrid volume planning. The implementation of these workflows revealed several challenges: software limitations and variability in treatment planning systems, inconsistencies in image registration file formats and communication challenges given multiple staff and site departments involved as well as site and central liaison. The automated MIM workflows and standardised trial nomenclature helped mitigate variability but required significant ongoing training and adaptation. RTT’s played a pivotal role in troubleshooting technical issues and ensuring data accuracy for study endpoint analysis. Conclusion: The FIG trial demonstrates the complexity and feasibility of incorporating FET-PET imaging into RT planning for glioblastoma through a structured workflow for a prospective multi-centre trial. It underscores the importance of technical infrastructure, interdepartmental coordination and the expanded and pivotal role of RTT’s in collaborative clinical trials. These learnings may inform future multi- modal imaging protocols and support broader implementation of advanced imaging in RT planning. Keywords: FET-PET, Radiotherapy, Trial Workflow References: 1. Koh E-S, Gan HK, Senko C, Francis RJ, Ebert M, Lee ST, et al. F-fluoroethyl-L-tyrosine (FET) in glioblastoma (FIG) TROG 18.06 study: protocol for a prospective, multicentre PET/CT trial. BMJ Open. 2023;13:e071327. doi:10.1136/bmjopen-2022-071327.
Digital Poster Highlight 771
Projecting the Return of Investment for Advanced Practice Radiation Therapist Roles Across Four Oncology Streams: An Institutional Planning Model.
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