The SWAN Project team were amongst the first internationally to instigate collation of digital radiotherapy treatment planning data from multi-centre clinical trials. This has enabled the group to develop the processes for assessing the quality of planning in trials, and for associating aspects of the data (including the assessment of its quality) to the outcomes for trial participants. The systems developed by the group have been used to support multiple clinical trials and are now used for broad outcomes analysis using complex three-dimensional information regarding radiotherapy dose, patient anatomy and organ function.

Current radiotherapy approaches utilise a process of delineation to segment individual parts of anatomy (i.e. create "structures") as a guide to treatment planning. The SWAN Project is investigating methods of guiding planning without the need for this segmentation step, describing individual "voxels" describing the patient via likelihoods of being a part of any particular organ. This approach should account for the significant uncertainties associated with that segmentation process, provide a more accurate account of the relationship between radiotherapy dose and the effect of that dose, and enable a very sophisticated approach to describing and planning the radiation dose history for an individual patient.

The SWAN Project is involved in assessing the use of advanced tracers for positron emission tomography (PET) imaging for guiding the treatment of adult glioma. As part of a national trial, imaging for trial participants treated nationally will be collated together with radiotherapy treatment planning data for the same patients in order to assess the impact of the imaging on the radiotherapy treatment and, subsequently, on the outcomes for the study participants. The project is supporting the TROG 18.06 "FIG" trial.

In a program of research led from the University of Sydney and involving a collaboration spanning Australia and New Zealand, multiparametric magnetic resonance imaging (MRI) is being used to characterise cancer within a patient. This process will enable cancer spatial distributions to be "mapped", indicate the spatial characteristics of the disease in any new patient and correlate with molecular characteristics. The SWAN Project team is using that information to investigate how that mapping can be used to focus the radiotherapy dose for a patient, in a process known as "Biofocussed RadioTherapy" (BiRT).  This approach will enable an increase in dose to those parts of the tumour at most risk of recurrence, and a reduction of dose to healthy tissue at risk of radiation injury.

Advanced stage cancers are those where the disease at its primary site has metastasised and spread throughout the body. PET imaging is offering new ways of identifying this disease and, with the aid of computational tools, we can detect metastatic lesions in images and track their progress over time. There is global debate on the best use of these images and regarding standardised ways of classifying changes in disease that the images reveal. The SWAN Project team have focussed on the use of the PSMA imaging method for surveying advanced prostate cancer. We are working to bring disparate datasets together from around the world to ascertain what information from these images provides the most accurate information about the disease and its response.

Statistical and computational models for predicting outcomes from cancer therapy are generated from clinical and trial data all the time. However, they rarely find their way into clinical use, with a central reason being that they are never validated outside of the setting in which they were produced. The VALIDATE consortium is looking to shift outcomes modelling from an academic exercise to a clinically meaningful, impactful process that will have significant patient benefit. The consortium includes members from around the world who are developing the computational methods to test models in many different settings. Those models that can demonstrate their accuracy across those settings will become prime candidates to move into clinical use.