ESTRO 2023 - Abstract Book

S1354

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ESTRO 2023

clinician using a 1-5 scale (1=no output/crash, 2=poor/needs contouring from scratch, 3=OK/significant changes needed, 4=Good/Minor changes needed, 5=Very good/no changes needed). Results Figure 1 shows manual vs automatic CAA contours for the 10 validation patients. Mean Dice similarity coefficient for the whole CAA was 0.77±0.09. The clinician observer rated auto-contours as “Good” in 35/45 cases, with the remaining 10 cases rated as “OK”. The auto-contouring software ran successfully for all 198 CT scans used for robustness testing. Each auto-contour was produced in ~1s on an Intel Xeon 6134 CPU, although the full processing pathway took 30-60s (includes data in/output, cropping and post-processing).

Figure 1: Comparison of manual vs automatic CAA contours.

Conclusion A fast and robust auto-contouring solution for the CAA was developed and validated. Auto-contour quality was rated as “Good” in most cases, requiring minimal editing, allowing fast CAA contouring. Accurate and consistent CAA contouring will support the introduction of heart-sparing RT as part of the RAPID-RT study. This study will assess the impact of cardiac sparing RT on survival in patients having radical radiotherapy for lung cancer [DOI: 10.1016/j.clon.2021.12.017].

PO-1654 Does data curation matter in deep learning segmentation? Clinical vs edited GTVs in glioblastoma.

K. Hochreuter 1,2 , J.F. Kallehauge 3,2 , J. Ren 1,2,4 , S.S. Korreman 1,2,4 , S. Lukacova 2,4 , J. Nijkamp 1,2 , A.K. Trip 1

1 Aarhus University Hospital, Danish Center for Particle Therapy, Aarhus, Denmark; 2 Aarhus University, Department of Clinical Medicine, Aarhus, Denmark; 3 Aarhus University Hospital, Aarhus, Denmark, Danish Center for Particle Therapy, Aarhus, Denmark; 4 Aarhus University Hospital, Department of Oncology, Aarhus, Denmark Purpose or Objective In postoperative chemoradiotherapy for glioblastoma (GBM) patients, the GTV is defined as contrast enhancement on T1w MRI including the surgical cavity according to ESTRO guidelines. To automatically segment this GTV, deep learning (DL) models can be developed using clinical GTVs. However, clinical GTVs suffer from interobserver variation, which may impact DL-model performance. The aim of this study was to compare performance of a DL-model based on clinical GTVs to a DL-model based on edited GTVs.

Materials and Methods