Abstract
Introduction: Following a preliminary work validating the technological feasibility of an adaptive workflow with Ethos for whole-breast cancer, this study aims to clinically evaluate the automatic segmentation generated by Ethos. Material and methods: Twenty patients initially treated on a TrueBeam accelerator for different breast cancer indications (right/left, lumpectomy/mastectomy) were replanned using the Ethos ® emulator. The adaptive workflow was performed using 5 randomly selected extended CBCTs per patient. The contours generated by artificial intelligence (AI) included both breasts, the heart, and the lungs. The target volumes, specifically the tumor bed (CTV_Boost), internal mammary chain (CTV_IMC), and clavicular nodes (CTV_Nodes), were generated through rigid propagation. The CTV_Breast corresponds to the ipsilateral breast, excluding 5mm from the skin. Two radiation oncologists independently repeated the workflow and qualitatively assessed the accuracy of the contours using a scoring system from 3 (contour to be redone) to 0 (no correction needed). Quantitative evaluation was carried out using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), surface Dice (sDSC) and the Added Path Length (APL). The interobserver variability (IOV) between the two observers was also assessed and served as a reference. Lastly, the dosimetric impact of contour correction was evaluated. The physician-validated contours were transferred onto the plans automatically generated by Ethos in adaptive mode. The dose prescription was 52.2Gy in 18 fractions for the boost, 42.3Gy for the breast, IMC, and nodes. The CTV/PTV margin was 2mm for all volumes, except for the IMC (5mm). Dose coverage (D 98% ) was assessed for the CTVs, while specific parameters for organs at risk (OAR) were evaluated: mean dose and V 17Gy (relative volume receiving at least 17Gy) for the ipsilateral lung, mean dose and D 2cc (dose received by 2cc volume) for the heart, the contralateral lung and breast. Results: The qualitative analysis showed that no correction or only minor corrections were needed for 98.6% of AI-generated contours and 86.7% of the target volumes. Regarding the quantitative analysis, Ethos’ contour generation outperformed inter-observer variability for all structures in terms of DSC, HD, sDSC and APL. Target volume coverage was achieved for 97.9%, 96.3%, 94.2% and 68.8% of the breast, IMC, nodes and boost CTVs, respectively. As for OARs, no significant differences in dosimetric parameters were observed. Conclusion: This study shows high accuracy of segmentation performed by Ethos for breast cancer, except for the CTV_Boost. Contouring practices for adaptive sessions were revised following this study to improve outcomes.