ESTRO 2022 - Abstract Book

S41

Abstract book

ESTRO 2022

1 University Hospital, LMU Munich, Radiation Oncology, Munich, Germany; 2 University Hospital, LMU Munich, Radiation Oncology , Munich, Germany; 3 Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Radiation Oncology, Rome, Italy; 4 Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Radiation Oncoloogy, Rome, Italy; 5 German Cancer Consortium, (DKTK), Munich, Germany Purpose or Objective The introduction of MR Linacs into clinics has enabled online adaptive radiotherapy, at the cost of longer workflows, notably due to the need for online recontouring. The aim of this work was (1) the development of an AI-based segmentation of organs at risk (OARs) and the CTV for prostate cancer treatments at the 0.35 T MRIdian, (2) to examine the transferability of trained models between institutes, and (3) to compare the fraction contours propagated by the MRIdian treatment planning system (TPS) with the AI predictions. Materials and Methods MR images of 19 prostate cancer patients (19 planning + 240 fraction images) treated at our institution (cohort 1, C1) and 73 planning images acquired at a collaborating institution (cohort 2, C2) were included. The bladder, rectum and CTV were manually segmented on planning MRIs by radiation oncologists, while fraction contours were propagated by the TPS and corrected by physicians shortly before the irradiation. We trained a 3D U-Net on C2 planning data and tested the network performance using the Dice similarity coefficient (DSC), the average and 95th percentile Hausdorff distance (HD avg and HD 95 ) on 3 datasets: (i) 10 planning C2 images not used for training, (ii) 19 C1 planning images, (iii) 240 C1 fraction images. For the rectum, we evaluated slices up to 1.5cm above/below the PTV top/bottom. Additionally, for 5 C1 patients with 5 fractions each, we propagated the manual planning contours to the anatomy of the day without further corrections using a simulated workflow in the TPS. Finally, we divided the CTV test set into subgroups of grade I&II (10%) and III&IV (90%) cases, due to differences in inclusion of seminal vesicles. Post-prostatectomy patients were excluded from the CTV analysis. Results For OARs, the mean DSC, HD avg , and HD 95 for C2 and C1 planning images were comparable, while the performance for fractions decreased slightly (see Table 1 and Fig. 1). CTV predictions showed higher network performance for C2 than C1 data and higher performance for grade III&IV cases than I&II. For the bladder, apart from one case, network predictions were better than the TPS propagated contours, both with average DSC=0.91(0.11). The outlier cases were related to patients with limited bladder filling, which were absent in the C2 training set. For the rectum, average DSC pred =0.86(0.15) and DSC prop =0.88(0.16) were obtained.

Conclusion Results for OARs suggest model transferability between institutes. However, this does not apply to CTV. Worse scores for fraction images might suggest higher contour variability caused by time pressure during adaptation. The CTV model performs poorly for grades ≤ II suggesting that separate training may be required. TPS propagated contours show comparable quality to the network predictions, however, the analysis may be biased in favor of propagated contours, which were the basis for manual corrections leading to the ground truth. Acknowledgments: Wilhelm Sander-Stiftung

PD-0068 Investigating intensity augmentation for deep learning contouring on prostate contrast-enhanced CT

D. Balfour 1 , D. Boukerroui 1 , Y. McQuinlan 1 , R. Baggs 2 , J. Turner 1 , M. Battye 3 , P. Looney 1 , W. van Elmpt 4 , A. Dekker 5 , M. Gooding 1 1 Mirada Medical Ltd., Science, Oxford, United Kingdom; 2 Mirada Medical Ltd., Product, Oxford, United Kingdom; 3 Mirada Medical Ltd., Engineering, Oxford, United Kingdom; 4 Maastro Clinic, Physics Innovation, Maastricht, The Netherlands; 5 Maastro Clinic, Radiotherapy, Maastricht, The Netherlands

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