The REQUITE Project: Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-effects and Improve Quality of Life in Cancer Survivors

C. West*, D. Azria, J. Chang-Claude, S. Davidson, P. Lambin, B. Rosenstein, D. De Ruysscher, C. Talbot, H. Thierens, R. Valdagni, A. Vega, M. Yuille

*Corresponding author for this work

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Requite is using standardised forms and questionnaires, which are available for data collection in other studies. The centralised resource will be accessible to investigate relationships between different toxicity end points and quality of life and ways to identify better dosimetric predictors of toxicity. Deasy et al. [27] highlighted that the way to accelerate progress in improving radiotherapy toxicity predictive models is: ‘to begin storing high-quality datasets in repositories’ so that ‘data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors’. The requite project addresses the need for data pooling. Of course, the long-term goal of predictive model/assay research is to individualise cancer treatment, which will need decision aids for both radiation oncologists and their patients [28], including information on an individual's likely tumour response and their risk of side-effects. Widespread implementation will require a high level of evidence for benefit (e.g. From randomised trials), including cost-effectiveness and medico-legal issues will need to be addressed (e.g. Ce marking, food and drug administration approval). High-quality datasets from multicentre studies of routine cancer patients are essential to underpin progress in predictive model research.
Original languageEnglish
Pages (from-to)739-742
JournalClinical Oncology
Issue number12
Publication statusPublished - Dec 2014

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