ESTRO 2025 - Abstract Book

S2004

Clinical - Urology

ESTRO 2025

Conclusion: This analysis reveals insights into patients' uptake of MyChart and participation in PROMs at key times of radiotherapy among prostate cancer patients. The highest engagement was observed in the pre-RT phase, reflecting patients' initial willingness to share health information through MyChart. The declining participation by the end of RT and during follow-up reveals challenges in sustaining patient engagement over time.

Keywords: prostate cancer, patient participation, PROMs

2570

Poster Discussion Interaction of genetic drivers and dose surface mapping for rectal toxicity following prostate cancer radiotherapy Artemis Bouzaki 1 , Alan McWilliam 1,2 , Eliana Vasquez Osorio 1,2 , Sarah Kerns 3 , David Azria 4 , Marrie-Pierre Farcy Jacquet 5 , Ananya Choudhury 6,1 , Jenny Chang-Claude 7 , Alison Dunning 8 , Maerten Lambrecht 9 , Barbara Avuzzi 10 , Dirk de Ruysscher 11 , Petra Seibold 12 , Elena Sperk 13 , Christopher Talbot 14 , Ana Vega 15,16,17 , Liv Veldeman 18 , Adam Webb 14 , Barry Rosenstein 19 , Catharine West 20 , Eliana Gioscio 21 , Tiziana Rancati 21 1 Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom. 2 Advanced Radiotherapy, The christie NHS foundation trust, Manchester, United Kingdom. 3 Medical College of Wisconsin, Medical, Milwaukee, USA. 4 Federation Universitaire d'Oncologie Radiothérapie d'Occitanie Méditerranée, Univ Montpellier, Montpellier, France. 5 Federation Universitaire d'Oncologie Radiothérapie d'Occitanie Méditerranée, Institut du Cancer Du Gard (ICG), Nimes, France. 6 The Christie NHS foundation trust, The christie NHS foundation trust, Manchester, United Kingdom. 7 Division of Cancer Epidemiology, g. German Cancer Research Center (DKFZ), Heidelberg, Germany. 8 Centre for Cancer Genetic Epidemiology, University of Manchester, Cambridge, United Kingdom. 9 KU Leuven, KU Leuven, Leuven, Belgium. 10 Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 11 Department of Radiation Oncology (Maastro), Maastricht University Medical Center, Maastricht, Netherlands. 12 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 13 Department of Radiation Oncology, Universitätsmedizin Mannheim, Mannheim, Germany. 14 Department of Genetics & Cancer Sciences, University of Leicester, Leicester, United Kingdom. 15 Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS, Santiago de Compostella, Spain. 16 Fundación Pública Galega de Medicina Xenómica (FPGMX), Fundación Pública Galega de Medicina Xenómica (FPGMX, Santiago de Compostella, Spain. 17 Biomedical Network on Rare Diseases (CIBERER), Biomedical Network on Rare Diseases (CIBERER), Santiago de Compostella, Spain. 18 Gent University hospital, Gent university, Gent, Belgium. 19 Departments of Radiation Oncology & Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA. 20 Translational Radiotherapy Group, The christie NHS foundation trust, Manchester, United Kingdom. 21 Data Science unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Purpose/Objective: Rectal toxicity is a significant side effect of radiotherapy for prostate cancer, impacting patients' quality of life. Genome-wide association studies (GWAS) have identified potential single-nucleotide polymorphism (SNP) candidates associated with rectal toxicity following prostate cancer radiotherapy. However, traditional GWAS methodologies do not account for the radiotherapy dose to the organ, or, at best, include volumetric summary statistics and therefore lose the dose’s spatial information. Here, we propose using dose surface maps of the rectum to fully account for the spatial dose distribution and identify specific locations where SNPs modulate the radiotherapy dose, increasing the risk of rectal toxicity. Material/Methods: Data from 1,293 prostate cancer patients from the REQUITE study were used. Deep learning-based automatic segmentations ensured consistent rectum contouring. Rectum contour lengths were spatially normalised using linear transformations inferior and superior to the centre of the prostate, creating standardised two-dimensional