ESTRO 2024 - Abstract Book

S4210

Physics - Intra-fraction motion management and real-time adaptive radiotherapy

ESTRO 2024

[1] Ronneberger, Fischer, Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, 2015. [2] Myronenko, Andriy. “3D MRI brain tumor segmentation using autoencoder regularization.” Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. Springer International Publishing, 2019.

Funding - DFG, GRK2274

976

Digital Poster

Feasibility study of adaptive radiotherapy with Ethos for breast cancer

Jessica Prunaretty, Arthur Galand, Nicolas Mir, Aurélie Morel, Céline Bourgier, Norbert Ailleres, David Azria, Pascal fenoglietto

Institut du Cancer de Montpellier, Radiotherapy, Montpellier, France

Purpose/Objective:

The aim of this study was to assess the feasibility of online adaptive radiation therapy with Ethos for breast cancer.

Material/Methods:

The Ethos workflow for breast cancer was reproduced using a Varian Ethos emulator. This retrospective study included 20 breast cancer patients previously treated with TrueBeam. All had undergone breast surgery for different indications (right/left, lumpectomy/mastectomy) and were evenly divided between these four cases. For each patient, 5 extended CBCTs performed initially for the treatment, were randomly selected in order to simulate 5 adaptive sessions. IMRT plans with 13 fields and 6MV FFF were designed for each patient using the Eclipse TPS and imported into the Ethos® solution. Prescriptions for target volumes were 52.2 Gy for the tumor bed (boost) and 42.3 Gy for breast, the internal mammary chain (IMC) and the clavicular lymph nodes (CLN) in 18 fractions. CTV-PTV margins are set at 2 mm for all locations except the IMC, for which the margin is 5 mm. The dataset was used in an Ethos emulator to test the full adaptive workflow. The contours generated by artificial intelligence (AI) for the influencers (left and right breasts/chest walls and lungs, heart), and by elastic or rigid propagation for the target volumes (internal mammary chain (IMC) and clavicular lymph nodes (CLN)) were compared to the initial contours delineated by two physicians using two metrics: Dice similarity coefficient (DICE) and Hausdorff