ESTRO 2022 - Abstract Book

S151

Abstract book

ESTRO 2022

1 University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom; 2 The Christie NHS Foundation Trust, Department of Clinical Oncology, Manchester, United Kingdom; 3 University of Manchester, Division of Informatics, Imaging and Data Sciences, Manchester, United Kingdom; 4 The Christie NHS Foundation Trust, Department of Medical Oncology, Manchester, United Kingdom; 5 L'Institut du Cancer de Montpellier, Faculty of Medicine, Montpellier, France; 6 Vall d'Hebron Institute of Oncology (VHIO), Hereditary Cancer Genetics Group, Barcelona, Spain; 7 Fondazione IRCCS Istituto Nazionale dei Tumori, Prostate Cancer Program, Milan, Italy; 8 Mount Sinai, Department of Radiation Oncology, New York, USA; 9 Maastricht University Medical Center, Department of Radiation Oncology, Maastricht, The Netherlands; 10 Universitätsmedizin Mannheim, Clinical Trials Unit Mannheim Cancer Center, Mannheim, Germany; 11 University of Leicester, Department of Genetics and Genome Biology, Leicester, United Kingdom; 12 Fondazione IRCCS Isituto Nazionale dei Tumori, Department of Radiation Oncology, Milan, Italy; 13 Fundación Pública Galega de Medicina Xenómica, University Hospital Santiago de Compostela, Molecular Medicine Service, Santiago de Compostela, Spain; 14 Ghent University Hospital, Department of Radiation Oncology, Ghent, Belgium; 15 German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany; 16 German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany Purpose or Objective Manual contouring adds to the RT planning workload, thus atlas-based auto-contouring tools (ABAC) have been explored for several sites. Young women (<36yo) treated for lymphoma with supradiaphragmatic RT have an increased risk of developing breast cancer (BC) compared to the general population, and would benefit from time of treatment breast contouring via breast ABAC to minimise RT dose. However, there is limited published research in breast ABAC for non-BC patients. Here, we evaluate the performance of ABAC developed on BC patients to contour breast tissue on young lymphoma patients, and hypothesise that they will perform worse than ABAC specifically developed for lymphoma patients. Materials and Methods ABAC were built on 20 templates from 2 patient groups, including 10 female lymphoma patients (18-36yo, 8/10 arms down) treated with supradiaphragmatic RT (ABAC lymph ) and 10 female BC patients (36-75yo, all arms up, 8/10 right-sided breast RT) from the REQUITE study (ABAC breast ). All contours were manually drawn by expert breast oncologists following ESTRO guidelines without the 5mm crop from skin. A leave-one-out (LOO) approach in each group was implemented in RayStation v6.99. Additionally, each LOO tool was applied to the other diagnostic group, generating 10 extra contours per patient (e.g. each lymphoma patient had 1 ABAC lymph and 10 ABAC breast contours). ABAC performance was evaluated using mean distance to agreement (meanDTA) and Dice similarity coefficient (DSC). Metrics for the contour generated using the LOO approach (i.e. using the other 9 patients in the same group) were compared to the averaged metric of the 10 extra contours (each generated using 9 templates from the other group). Results Results are reported for left and right breasts combined (see table 1). Despite different age ranges, no significant difference in breast volume or average breast density was observed between the two diagnostic groups. ABAC breast applied to BC patients performed best; DSC median 0.89 (range 0.75-0.96), meanDTA 0.25cm (0.13-0.90) (see fig 1). Unexpectedly, when applied to lymphoma patients, ABAC lymph did not appear to perform better than ABAC breast ; DSC 0.86 (0.46-0.91) vs 0.86 (0.56-0.94), meanDTA 0.28cm (0.14-0.82) vs 0.24cm (0.18-0.65). We also observed poorer performance on the left breast than the right in all ABACs.