Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment
IntroductionFollowing 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 methodsTwenty patients initially treated on a TrueBeam accel...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1507806/full |
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| author | Jessica Prunaretty Fatima Mekki Pierre-Ivan Laurent Aurelie Morel Pauline Hinault Celine Bourgier David Azria Pascal Fenoglietto |
| author_facet | Jessica Prunaretty Fatima Mekki Pierre-Ivan Laurent Aurelie Morel Pauline Hinault Celine Bourgier David Azria Pascal Fenoglietto |
| author_sort | Jessica Prunaretty |
| collection | DOAJ |
| description | IntroductionFollowing 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 methodsTwenty 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 (D98%) was assessed for the CTVs, while specific parameters for organs at risk (OAR) were evaluated: mean dose and V17Gy (relative volume receiving at least 17Gy) for the ipsilateral lung, mean dose and D2cc (dose received by 2cc volume) for the heart, the contralateral lung and breast.ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-8ea2c6a889d84979b78947cd63dc9949 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-8ea2c6a889d84979b78947cd63dc99492025-08-20T02:22:06ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-12-011410.3389/fonc.2024.15078061507806Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatmentJessica PrunarettyFatima MekkiPierre-Ivan LaurentAurelie MorelPauline HinaultCeline BourgierDavid AzriaPascal FenogliettoIntroductionFollowing 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 methodsTwenty 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 (D98%) was assessed for the CTVs, while specific parameters for organs at risk (OAR) were evaluated: mean dose and V17Gy (relative volume receiving at least 17Gy) for the ipsilateral lung, mean dose and D2cc (dose received by 2cc volume) for the heart, the contralateral lung and breast.ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1507806/fullEthosauto-segmentationartificial intelligencebreast canceradaptive treatment |
| spellingShingle | Jessica Prunaretty Fatima Mekki Pierre-Ivan Laurent Aurelie Morel Pauline Hinault Celine Bourgier David Azria Pascal Fenoglietto Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment Frontiers in Oncology Ethos auto-segmentation artificial intelligence breast cancer adaptive treatment |
| title | Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment |
| title_full | Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment |
| title_fullStr | Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment |
| title_full_unstemmed | Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment |
| title_short | Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment |
| title_sort | clinical feasibility of ethos auto segmentation for adaptive whole breast cancer treatment |
| topic | Ethos auto-segmentation artificial intelligence breast cancer adaptive treatment |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1507806/full |
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