Please note that abstracts are reviewed by experts in the track and topic chosen. Submitting to the wrong topic may result in your abstract receiving a lower score.
If the main topic of your abstract is related to brachytherapy, the abstract should be submitted under the following ‘Brachytherapy’ topics:
- Head & neck, skin, eye
- Gastro-intestinal, paediatric brachytherapy, miscellaneous
- Urology - prostate, bladder, penis
- General brachytherapy
If the main topic of your abstract is clinical treatment and/or outcomes (e.g. tumour response/morbidity), the abstract should be submitted under the following ‘Clinical’ topics.
Prospective clinical trials should be identified as such in the title of the abstract and include The National Clinical Trial number where applicable.
- Head & neck
- Upper GI
- Lower GI
- Sarcoma/skin cancer/malignant melanoma
- Paediatric tumours
- Mixed sites/palliation
If your abstract does not fit in any of the above categories but is still within the topic of radiation oncology, the abstract should be submitted under the following ‘Interdisciplinary” topics.
These will be evaluated by the Scientific Programme Committee, including all track chairs.
- Education in radiation oncology
- Health economics & health services research
Abstracts submitted to this category may contain the broad range of aspects related to health economics and health services research, such as availability and access to radiotherapy, cost and health outcomes, and cost-effectiveness. In addition, we would particularly welcome abstracts that relate to value-based health care in radiation oncology, including how to support the equitable introduction of evidence-based innovations.
- Global health
- Other (topic of relevance for radiation oncology, NOT related to any other category)
If the main topic of your abstract is related to medical physics, including advanced modelling/statistics etc. (and not focusing on clinical outcomes), the abstract should be submitted under the following ‘Physics’ topics.
Under each topic, machine learning based research may be submitted. There is however also a specific topic fully dedicated to machine learning and clinical applications (excluding imaging and segmentation), see below.
Abstracts submitted under this topic may report on autocontouring, as well as automated image based identification of structures/landmarks.
- Detectors, dose measurement and phantoms
Abstracts submitted to this category may contain classical experimental studies on characterisation of new detectors, phantom development, dose measurement and measurement protocols including radiation protection.
- Dose calculation algorithms
Abstracts submitted to this category may contain theoretical studies on dose calculation. Treatment planning studies should NOT be submitted to this topic but should to the categories “Optimisation, algorithms and applications”.
- Dose prediction, optimisation and applications of photon and electron planning
This category includes studies related to optimisation strategies and algorithms for radiotherapy treatment planning, including IMRT, VMAT, tomotherapy and others, as well as radiotherapy treatment planning, including comparison studies and applications in general for IMRT, VMAT, tomotherapy, and others. Machine learning models may be considered, but may be better suited under “Machine learning models and clinical applications”
- Image acquisition and processing
This category aims at abstracts describing studies related to development and validation of (novel) image acquisition and analysis strategies including CT, MRI, PET and potentially other imaging techniques used for radiotherapy planning, prescription, adaptation and outcome prediction . Machine learning abstracts on this category should specifically be submitted to this category and not to Machine learning models and clinical applications (excluding imaging and segmentation)
- Inter-fraction motion management and offline adaptive radiotherapy
This topic invites abstracts related to adaptive radiotherapy with a focus on offline, inter-fraction adaptation and image-guided RT. Development and implementation of new adaptation strategies including patient validation can also be submitted.
- Intra-fraction motion management and real-time adaptive radiotherapy
This topic invites abstracts reporting on studies focused on intra-fraction motion management, including online and real-time adaptive radiotherapy.
- Machine learning models and clinical applications (excluding imaging and segmentation)
This topic is specific to machine learning models, focussing on the machine learning aspects of the research. It can range from really new models and DL concepts and incorporation of eg uncertainty to new clinical experiences. It may involve dose prediction, PSQA, outcome prediction etc.
- Optimisation, algorithms and applications for ion beam treatment planning
Abstracts for this category invite studies related to optimisation strategies and algorithms for radiotherapy treatment planning, including, proton therapy, heavy ion therapy and others, as well as radiotherapy treatment planning (including comparison studies such as protons vs photons and others with ion beams) and applications in proton therapy, heavy ion therapy and others.
- Quality assurance and auditing
Abstracts submitted under this topic may report on quality assurance and auditing with a focus on physics and technology including in clinical protocols and trials.
- Radiomics, functional and biological imaging and outcome prediction
Here, studies reporting on physical aspects including image acquisition, analysis methods and/or independent validation of quantitative functional and biological imaging. This may include correlation studies with respect to endpoints during or after fractionated radiotherapy and evaluation of current or future radiotherapy interventions based on quantitative imaging. Includes reports on studies related to outcome/prediction modelling based on classical risk predictions or radiomics, including methodological considerations and statistics. If the focus is on the modelling methodology or advanced statistics for outcome prediction, then the abstract should be submitted here. If the focus is on the outcomes only then the abstract should be submitted to the clinical track.
If the main topic of your abstract is related to radiobiology/experimental radiotherapy using in vivo, in vitro or molecular technique/models, the abstract should be submitted under the following ‘Radiobiology’ topics.
- Normal tissue radiobiology
- Tumour radiobiology
If the main topic of your abstract is related to RTT activities, the abstract should be submitted under the following ‘RTT’ topics.
- Patient care, preparation, immobilisation and IGRT verification protocols
- Patient experience and quality of life
- RTT treatment planning, OAR and target definitions
- RTT service evaluation, quality assurance and risk management
- RTT education, training, advanced practice and role developments