ESTRO 36 Abstract Book

S248 ESTRO 36 2017 _______________________________________________________________________________________________

randomised trial design moving from pilot to phase II to phase III, with the aim of reducing locoregional failure. The primary endpoint for all 3 trials is 3 year local regional failure, key secondary outcomes will focus on patient reported outcome measures.

normalisation in quantitative data. The use of a standard ontology which may need to be actively developed can help streamline this. Data quality will be an ongoing challenge. Ideally every parameter within a dataset will be correctly recorded, curated and with minimal variation in any scoring scales or assessment criteria. Particularly within clinical practice datasets it is highly unlikely that every parameter within a dataset is correct although this varies both between and within centres. There are two practical issues to be considered, the first that of missing data where particular parameters are not recorded for all patients or not recorded for some patients and the second that of incorrect data entries. Although a complete full quality dataset is always preferred imputation approaches can be used successfully to address missing data, increase dataset size and thus increase model confidence. Incorrect data entries are more challenging, however if incorrect data entries are random and datasets are large then the impact of this will be minimised and seen primarily in model confidence parameters. Although it is important to be aware of the limitations and challenges of use of data, there is growing evidence that use of data can improve our knowledge and understanding of radiotherapy. SP-0473 From Genomics and Radiogenomics data to a better RT A. Vega 1 1 Fundación Pública Galega Medicina Xenómica, Hospital Clinico Santiago de Compostela, Santiago de Compostela, Spain The completion of The Human Genome Project in 2001, after more than 10 years of international collaborative efforts along with progress in technology, heralded an era of enormous advances in the field of genetics. The study of the genetic susceptibility behind the different response of the irradiated tissue of patients treated with radiotherapy, known today as Radiogenomics, is an example of this. One of the major aims of Radiogenomics is to identify genetic variants, primarily common variation (SNPs), associated with normal tissue toxicity following Radiotherapy. A large number of candidate-gene association studies in patients with or without Radiotherapy side-effects have been published in recent years. These studies investigated SNPs in genes related to radiation pathogenesis, such as DNA damage, DNA repair, tissue remodeling and oxidative stress. Most of the studies suffered from methodological shortcomings (small sample sizes, lack of adjustment for other risk factors/covariates or multiple testing). The Human Genome Project and the subsequent International HapMap Project, provided extremely valuable information on the common variation of human DNA in different populations. This information and the development of high-density SNP arrays, in which all genome variability is considered (from 500000 to a few million SNPs), enabled Genome Wide Association Studies (GWAS) and an hypothesis-free case-control approach. GWASs are a major breakthrough in the attempts to unravel the genetics of common traits and diseases. Simultaneous analysis of thousands of variants requires a large sample size to achieve adequate statistical power. The need for a large number of samples makes it essential to collaborate and share data. A Radiogenomics Consortium (RGC) was established in 2009 with investigators from throughout the world who shared an interest in identifying the genetic variants associated with patient differences in response to radiation therapy. To date, RGC collaborative large-scale projects have led to statistically-powered gene-association studies, as well as Radiogenomic GWASs. The recent availability of Next Generation Sequencing (NGS) and advances in Bioinformatics, have promoted major initiatives such as The 1000 Genomes Project and The Cancer Genome Atlas

Joint Symposium: ESTRO-RANZCR: Big data to better radiotherapy

SP-0472 The pros, cons, process and challenges for achieving better radiotherapy through data -an introduction L.C. Holloway 1,2,3,4 1 Ingham Institute and Liverpool and Macarthur Cancer Therapy Centres, Medical Physics, Sydney, Australia 2 University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia 3 University of Sydney, Institute of Medical Physics, Sydney, Australia 4 University of New South Wales, South West Sydney Clinical School, Sydney, Australia The magnitude and use of data is expanding in many areas including medicine. The opportunities for using data to improve our knowledge and understanding are many and varied including demographic, disease and outcome investigations. Within current radiotherapy practice data may be collected in a very rigorous approach within for instance a clinical trial framework and data is also collected in an ongoing fashion during standard clinical practice. It is possible for us to gain knowledge from both rigorously collected data and clinical practice data. The gold standard of randomised clinical trial (RCT) evidence is only provided from 2-3% of retrospective patients who have been enrolled in RCTs and is only directly applicable to a limited number of patients due to strict trial eligibility criteria, necessary to ensure trial rigour. Clinical practice data may provide us with the opportunity to develop additional evidence to support evidence from RCTs, utilising data from potentially all previous patients and including patients who do not fit RCT eligibility criteria. Considering data from both RCTs and clinical practice may also enable us to learn from the differences between the two. Different approaches to learning from data have been undertaken. These range from include common statistical approaches to machine learning approaches. All data learning approaches require the development and then validation of models. Validation must be undertaken carefully to ensure that the developed model is validated on independent datasets. To utilise data we need data; ideally large datasets from multiple treatment centres with varied clinical practice and of high quality. Achieving this requires a number of challenges to be addressed. Collecting large datasets can be very challenging in the medical field due to ethics, privacy and national and international regulations as well as the practical and technical challenges of collecting large datasets ( e.g. when using multiple medical images). One approach to addressing this is termed ‘distributed learning’ where datasets remain within the local treatment centres. Computer algorithms can then be used to both assess (as in practice comparison and demographic studies) and learn from (as in model development providing evidence for future treatment decisions) these datasets. The requirement for varied clinical practice generally requires international datasets where local treatment guidelines may vary between countries. This requires collaboration between centres and an active effort to ‘translate’ between practices to ensure that data items considered are consistent between the datasets. Translation is necessary for simple items such as language differences but also more challenging differences such as different scoring scales or different approaches to