ESTRO 2024 - Abstract Book

S110 ESTRO 2024 of life and healthcare utilisation. Longitudinal PROM data, collected over time, through either randomly sampling people as part of cancer registries or recruiting large scale patient cohorts can provide useful population level data on symptoms and their impact on patient’s quality of life compared to that of a non-cancer population. PROM data is also linked with treatment records and public health data sets. These data include more diverse populations than those studied in clinical trials, providing real world evidence that can inform clinical practice [2]. Such data allows the opportunity to identify patterns in symptoms and compare patient experience assessing for those at higher risk and aiding communication and decision making. Longitudinal measurement is a key element of PROMs with the need to collect baseline scores prior to treatment and data collected regularly for months and years after treatment. Studies have demonstrated that baseline PROM scores can directly affect treatment outcomes, for example patients who report depression and poorer physical health prior to therapy are more likely to have treatment discontinuation. Analysing symptom trajectories through PROMs clarifies treatment late effects management, as this allows the opportunity to identify when people should be assessed and develop actionable intervention. Identification of symptom clusters may also offer opportunities for understanding how symptoms interact and provide approaches to radiotherapy syndromes, such as pelvic radiation disease. Challenges to long term PROM data collection are the high investment needed to collect PROMs over time but also the lack of normative comparative data. PROM data collected as part of population and cohort studies can provide useful insights into rare cancers, difficult to reach populations and therapy outcomes. Studies that include PROMs increase the quality of information and give greater granularity and understanding of treatment long term and late effects. This knowledge is critical for informing long term follow-up, new treatment strategies and healthcare interventions for late effects to improve outcomes for cancer survivors. 1. Di Maio M, Basch E, Denis F, Fallowfield LJ, Ganz PA, Howell D, Kowalski C, Perrone F, Stover AM, Sundaresan P et al : The role of patient-reported outcome measures in the continuum of cancer clinical care: ESMO Clinical Practice Guideline . Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2022, 33 (9):878-892. 2. Faithfull S, Greenfield D: Cancer survivor late-effects, chronic health problems after cancer treatment: what's the evidence from population and registry data and where are the gaps? Curr Opin Support Palliat Care 2024, 18 (1):55-64. Invited Speaker

3504

Statistics for radiation therapy insiders: What to use and when?

Ivan R Vogelius

Rigshospitalet, Dept of Oncology, Copenhagen, Denmark. University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark

Abstract:

The title is ambitious considering that a formal statistical training takes years and I cannot promise a full solution to all your problems. After a brief foundation, we will focus on practical examples - good and bad - to discuss the need for appropriate statistical approaches in the research and development work of a medical physicist.