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Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk

Tang, Vivi ; Wieslander, Elinore LU ; Haghanegi, Mahnaz ; Kjellén, Elisabeth LU and Alkner, Sara LU (2025) In Clinical and Translational Radiation Oncology 53.
Abstract

Introduction: Target volume delineation is crucial in breast cancer radiotherapy planning but involves significant interobserver variability. Deep learning (DL) models may reduce this variability, saving time and costs. However, current DL-models do not consider clinical data, such as tumor location and patient comorbidity, to adjust the target and reduce dose to organs at risk (OAR). This study compares clinically defined target volumes to those generated by a DL-model in terms of size, geometric overlap, and dose to OAR. Method: For a hypothetical breast cancer patient, we compared target volumes constructed by Swedish radiotherapy clinics and two DL-models, Raystation and MVision. Geometrical overlap was evaluated, as well as the... (More)

Introduction: Target volume delineation is crucial in breast cancer radiotherapy planning but involves significant interobserver variability. Deep learning (DL) models may reduce this variability, saving time and costs. However, current DL-models do not consider clinical data, such as tumor location and patient comorbidity, to adjust the target and reduce dose to organs at risk (OAR). This study compares clinically defined target volumes to those generated by a DL-model in terms of size, geometric overlap, and dose to OAR. Method: For a hypothetical breast cancer patient, we compared target volumes constructed by Swedish radiotherapy clinics and two DL-models, Raystation and MVision. Geometrical overlap was evaluated, as well as the impact of differences in target delineation on dose to OAR. Treatment plans for locoregional vs. breast-only 3D-conformal radiotherapy were generated. Results: CTV-structures for the breast, lymph nodes level I-IV, and internal mammary nodes were available for 10, 11, and 14 centers respectively. Volume of the CTV-breasts varied between 770–890cc, and the total CTV-volumes (breast + lymph nodes) between 875–1003cc. The DL-models did not constitute the largest nor smallest breast or total CTV-volumes, and geometric overlap between structures was relatively good. Evaluating dose to OAR from dose plans based on the respective CTV-volumes for locoregional radiotherapy, this was comparable between the DL-models and the mean of the CTVs generated by the clinics. In radiotherapy of only the breast, the CTV-breasts constructed by the DL-models gave the highest heart doses due to their proximity to the chest wall, affecting field angle choices. No difference was seen in dose to the ipsilateral lung, thyroid gland, or humeral head. Conclusion: DL-models for target delineation have great potential. However, their introduction must be closely monitored since even small differences compared to clinical standards may affect doses to OAR in 3D conformal breast cancer radiotherapy.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AI contouring, Breast cancer, Deep learning segmentation, Dosimetric data, Radiotherapy, Target volume delineation
in
Clinical and Translational Radiation Oncology
volume
53
article number
100986
publisher
Elsevier
external identifiers
  • pmid:40529410
  • scopus:105007334935
ISSN
2405-6308
DOI
10.1016/j.ctro.2025.100986
language
English
LU publication?
yes
id
c3056aa9-9734-405f-a041-d6bed0a4bbef
date added to LUP
2026-01-08 11:26:53
date last changed
2026-01-09 03:00:06
@article{c3056aa9-9734-405f-a041-d6bed0a4bbef,
  abstract     = {{<p>Introduction: Target volume delineation is crucial in breast cancer radiotherapy planning but involves significant interobserver variability. Deep learning (DL) models may reduce this variability, saving time and costs. However, current DL-models do not consider clinical data, such as tumor location and patient comorbidity, to adjust the target and reduce dose to organs at risk (OAR). This study compares clinically defined target volumes to those generated by a DL-model in terms of size, geometric overlap, and dose to OAR. Method: For a hypothetical breast cancer patient, we compared target volumes constructed by Swedish radiotherapy clinics and two DL-models, Raystation and MVision. Geometrical overlap was evaluated, as well as the impact of differences in target delineation on dose to OAR. Treatment plans for locoregional vs. breast-only 3D-conformal radiotherapy were generated. Results: CTV-structures for the breast, lymph nodes level I-IV, and internal mammary nodes were available for 10, 11, and 14 centers respectively. Volume of the CTV-breasts varied between 770–890cc, and the total CTV-volumes (breast + lymph nodes) between 875–1003cc. The DL-models did not constitute the largest nor smallest breast or total CTV-volumes, and geometric overlap between structures was relatively good. Evaluating dose to OAR from dose plans based on the respective CTV-volumes for locoregional radiotherapy, this was comparable between the DL-models and the mean of the CTVs generated by the clinics. In radiotherapy of only the breast, the CTV-breasts constructed by the DL-models gave the highest heart doses due to their proximity to the chest wall, affecting field angle choices. No difference was seen in dose to the ipsilateral lung, thyroid gland, or humeral head. Conclusion: DL-models for target delineation have great potential. However, their introduction must be closely monitored since even small differences compared to clinical standards may affect doses to OAR in 3D conformal breast cancer radiotherapy.</p>}},
  author       = {{Tang, Vivi and Wieslander, Elinore and Haghanegi, Mahnaz and Kjellén, Elisabeth and Alkner, Sara}},
  issn         = {{2405-6308}},
  keywords     = {{AI contouring; Breast cancer; Deep learning segmentation; Dosimetric data; Radiotherapy; Target volume delineation}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Clinical and Translational Radiation Oncology}},
  title        = {{Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk}},
  url          = {{http://dx.doi.org/10.1016/j.ctro.2025.100986}},
  doi          = {{10.1016/j.ctro.2025.100986}},
  volume       = {{53}},
  year         = {{2025}},
}