ESTRO 36 Abstract Book

S308 ESTRO 36 _______________________________________________________________________________________________

patient's perspective provides a holistic and a more comprehensive assessment of treatment, and PROMs are increasingly being seen as a way to improve practice by enhancing communication, improving management of symptoms associated with disease or treatments, as well as identifying patients' care needs. With the increase in long-term remote follow-up of patients PROMs play an important role as they offer the opportunity to assess and address the health concerns or health-related quality of life (HRQOL) issues of individual patients [1]. Other important clinical applications of PROMs include aiding treatment choices as well as identifying high risk patients who may have poorer long-term health-related outcomes [2, 3]. These are all key challenges of modern oncology, and PROMs play a strategic role in this as they enable the tailoring of treatments according to the priorities, risks or concerns of individual patients. The successful application of PROMs requires a deeper understanding of the methods for extracting information carried within PROMs [4]. PROMs data are complex, with a large number of variables (HRQOL, symptoms, function, bother, performance or health concerns) measured on different scales (with different levels, ratios or frequencies). Symptom clusters are groups of 3 or more correlated symptoms that occur together, and this is stable over time [5, 6]. Symptom clusters can be easily determined specifically to each dataset or clinical trial [7]. This can be used as a method of grouping symptoms for the purpose of summarising PROMs and extracting meaningful information. The advantage of exploring symptom clusters within a dataset is that it allows a study specific method of grouping symptoms. Because of this symptom clusters have the potential to improve sensitivity and specificity to symptom grouping. Only items that are strongly correlated, and so measure the same underlying health concern, are included in summative scores. This can be utilised in PROMs data modelling or clinical decision making. In clinical trials PROMs are often seen as a research tool and it can be challenging to deliver real-time clinical applications. PROMs can be difficult for patients to complete, and missing data is another common problem when analysing and interpreting PROMs [8]. Some of the causes of missing data include the complexity of long and multiple PROMs questionnaires, lack of feedback following the delivery of PROMs, difficulty in understanding questions or language issues (potentially associated with migration), patients missing their appointments or dropping-out of studies, and intermittent missingness when patients fail to complete some of the questions. All this contributes to the degree of missing data and in turn a reduction in sample size, limited analytical applications or even the risk of biased results. As treatments evolve and the characteristics of patient populations change, study specific approaches to analysing PROMs are warranted. The correlation and grouping of items, missing data, and the ceiling or floor effect in collected data should all be investigated for each study when interpreting and analysing PROMs. This may advance PROMs data analysis and lead to the extraction of more relevant and meaningful information. 1. Horwitz EM, Bae K, Hanks GE, Porter A, Grignon DJ, Brereton HD et al. Ten-Year Follow-Up of Radiation Therapy Oncology Group Protocol 92-02: A Phase III Trial of the Duration of Elective Androgen Deprivation in Locally Advanced Prostate Cancer. Journal of Clinical Oncology. 2008;26(15) 2. Weldring T, Smith SMS. Patient-Reported Outcomes (PROs) and Patient-Reported Outcome Measures (PROMs). Health Services Insights. 2013;6 3. Warrington L, Absolom K, Velikova G. Integrated care pathways for cancer survivors - a role for patient-reported outcome measures and health informatics. Acta Oncol. 2015;54(5)

4. Faithfull S, Lemanska A, Chen T. Patient-reported Outcome Measures in Radiotherapy: Clinical Advances and Research Opportunities in Measurement for Survivorship. Clin Oncol. 2015;27(11) 5. Aktas A. Cancer symptom clusters: current concepts and controversies. Curr Opin Support Palliat Care. 2013;7(1) 6. Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum. 2001;28(3) 7. Skerman HM, Yates PM, Battistutta D. Multivariate methods to identify cancer-related symptom clusters. Res Nurs Health. 2009;32(3) 8. Gomes M, Gutacker N, Bojke C, Street A. Addressing Missing Data in Patient-Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance. Health Econ. 2016;25(5) SP-0583 Moderate hypofractionation in prostate cancer: what have we learnt from phase 3 trials D.P. Dearnaley 1 1 Institute of Cancer Research, Academic Radiotherapy, London, United Kingdom Evidence has accumulated suggesting that prostate cancer (PCa) may be particularly sensitive to radiation fraction size. This has considerable implications for the delivery of radical radiation treatments suggesting that shorter treatments using higher dose/fraction schedules might improve the therapeutic ratio and make treatment more convenient for patients as well as using radiotherapy resource more effectively. Four large randomised controlled trials testing modest hypofractionation for localised PCa have reported efficacy and side effect outcomes within the last year (1-4) . The largest trial, CHHiP, which included 3216 patients compared standard fractionation (SFRT) using 2.0Gy daily fractions (f) (total dose 74Gy) with two experimental hypofractionated (HFRT) schedules using 3.0Gy/f (total doses of 60Gy and 57Gy) (1) . The trial used a non-inferiority design and demonstrated that HFRT at 60 Gy was non–inferior to SFRT. Five year disease control rates defined by biochemical (PSA)/clinical failure free outcome were for HFRT (60Gy) 90.6% (95% confidence intervals 88.5 - 92.3) compared with SFRT 88.3% (86.0 - 90.2) (hazard ratio 0.84, (95% CI: 0.65 – 1.07)); treatment related toxicities were low and similar. A complementary study design was used in the PROFIT trial (2) which included 1206 patients and compared SFRT using 2.0Gy/f (total dose 78Gy) with the same HFRT schedule of 3.0Gy/f (total dose 60Gy). HFRT was again shown to be non-inferior to SFRT with identical 21% biochemical/clinical failure rates at 5 years. In PROFIT gastro-intestinal side effects were increased in the SFRT group compared with HFRT group probably due to the higher SFRT dose given compared with CHHiP. Intensity modulated radiotherapy methods (IMRT) using either forward or inverse planning with a 3 part simultaneous integrated boost were used in all patients in the CHHiP trial. IMRT/IGRT methods were used in the PROFIT trial. A key difference between the trials was the use of 6 months neoadjuvant androgen deprivation therapy (ADT) in CHHiP whilst RT alone was used in PROFIT which probably explains the 11% higher biochemical control rate in CHHiP. Both investigator groups suggested that HFRT (60Gy/20f in 4 weeks) could be considered a new standard of care. In contradistinction authors of the HYPRO study (3) came to different conclusions testing dose escalated HFRT. 804 patients received either SFRT 78Gy in 2Gy daily fractions or HFRT giving 64Gy in 3·4Gy fractions but importantly treating with three fractions per week and therefore protracting overall treatment time (OTT). The gain in tumour control was smaller than might Symposium: Hypofractionation in prostate cancer