Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 13:13:1274082.
doi: 10.3389/fonc.2023.1274082. eCollection 2023.

Feasibility study of adaptive radiotherapy with Ethos for breast cancer

Arthur Galand  1 , Jessica Prunaretty  1 , Nicolas Mir  1 , Aurélie Morel  1 , Céline Bourgier  1 , Norbert Aillères  1 , David Azria  1 , Pascal Fenoglietto  1
Affiliations

Feasibility study of adaptive radiotherapy with Ethos for breast cancer

Arthur Galand et al. Front Oncol. .

Abstract

Purpose: The aim of this study was to assess the feasibility of online adaptive radiotherapy with Ethos for breast cancer.

Materials and methods: 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, with five extended cone beam computed tomography (CBCT) scans per patient. 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 and lungs, heart) and elastic or rigid propagation for the target volumes (internal mammary chain (IMC) and clavicular lymph nodes (CLNs)) were compared to the initial contours delineated by the physician using two metrics: Dice similarity coefficient (DICE) and Hausdorff 95% distance (HD95). The repeatability of influencer generation was investigated. The times taken by the emulator to generate contours, optimize plans, and calculate doses were recorded. The quality of the scheduled and adapted plans generated by Ethos was assessed using planning target volume (PTV) coverage, homogeneity indices (HIs), and doses to organs at risk (OARs) via dose-volume histogram (DVH) metrics. Quality assurance (QA) of the treatment plans was performed using an independent portal dosimetry tool (EpiQA) and gamma index.

Results: On average, the DICE for the influencers was greater than 0.9. Contours resulting from rigid propagation had a higher DICE and a lower HD95 than those resulting from elastic deformation but remained below the values obtained for the influencers: DICE values were 0.79 ± 0.11 and 0.46 ± 0.17 for the CLN and IMC, respectively. Regarding the repeatability of the influencer segmentation, the DICE was close to 1, and the mean HD95 was strictly less than 0.15 mm. The mean time was 73 ± 4 s for contour generation per AI and 80 ± 9 s for propagations. The average time was 53 ± 3 s for dose calculation and 125 ± 9 s for plan optimization. A dosimetric comparison of scheduled and adapted plans showed a significant difference in PTV coverage: dose received by 95% of the volume (D95%) values were higher and closer to the prescribed doses for adapted plans. Doses to organs at risk were similar. The average gamma index for quality assurance of adapted plans was 99.93 ± 0.38 for a 3%/3mm criterion.

Conclusion: This study comprehensively evaluated the Ethos® adaptive workflow for breast cancer and its potential technical limitations. Although the results demonstrated the high accuracy of AI segmentation and the superiority of adapted plans in terms of target volume coverage, a medical assessment is still required.

Keywords: AI; CBCT; adaptive radiotherapy; breast cancer; ethos.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision

Figures

Figure 1
Figure 1
DICE values resulting from the comparison between contours generated by Ethos and contours delineated by physicians. The influencer (from AI generation) DICE results are displayed in purple, while the target volume DICE results are displayed in gray (for rigid propagation) and green (for elastic deformation). Bars are the standard deviations. DICE, Dice similarity coefficient; AI, artificial intelligence.
Figure 2
Figure 2
Hausdorff distance 95% (HD95) values resulting from the comparison between contours generated by Ethos and contours delineated by physicians. The influencer (from AI generation) HD95 results are displayed in purple, while the target volume HD95 results are displayed in gray (for rigid propagation) and green (for elastic deformation). AI, artificial intelligence.
Figure 3
Figure 3
Comparison between the physician contour and the rigid propagation contour for the internal mammary chain (left) and the clavicular lymph nodes (right).
Figure 4
Figure 4
Box plots of doses received by the heart, the ipsilateral lung (I_Lung), and the contralateral lung (C_lung) for reference, adapted, and scheduled plans (blue, gray, and pink, respectively).
Figure 5
Figure 5
Box plots of gamma index pass rate results for all adapted plans with 3%/3mm, 2%/2mm, and 1%/1mm criteria.
See this image and copyright information in PMC

References

    1. Sonk J-J, Aznar M, Rasch C. Adaptive radiotherapy for anatomical changes. Semin Radiat Oncol (2019) 29(3):245–57. doi: 10.1016/j.semradonc.2019.02.007 - DOI - PubMed
    1. Yan D, Vicini F, Wong J, Martinez A. Adaptive radiation therapy. Phys Med Biol (1997) 42(1):123–32. doi: 10.1088/0031-9155/42/1/008 - DOI - PubMed
    1. Borck KK. Adaptive radiotherapy: moving into the future. Semin Radiat Oncol (2019) 29(3):181–4. doi: 10.1016/j.semradonc.2019.02.011 - DOI - PMC - PubMed
    1. Qiu Z, Olberg S, den Hertog D, Ajdari A, Bortfeld T, Pursley J. Online adaptive planning methods for intensity-modulated radiotherapy. Phys Med Biol (2023) 68(10):10TR01. doi: 10.1088/1361-6560/accdb2 - DOI - PMC - PubMed
    1. Yan D. Adaptive radiotherapy: merging principle into clinical practice. Semin Radiat Oncol (2010) 20(2):79–83. doi: 10.1016/j.semradonc.2009.11.001 - DOI - PubMed

LinkOut - more resources