Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk
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...
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Elsevier
2025-07-01
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| Series: | Clinical and Translational Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405630825000783 |
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| author | Vivi Tang Elinore Wieslander Mahnaz Haghanegi Elisabeth Kjellén Sara Alkner |
| author_facet | Vivi Tang Elinore Wieslander Mahnaz Haghanegi Elisabeth Kjellén Sara Alkner |
| author_sort | Vivi Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-53c8a2c6a089472a85c95f275895abcc |
| institution | DOAJ |
| issn | 2405-6308 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Clinical and Translational Radiation Oncology |
| spelling | doaj-art-53c8a2c6a089472a85c95f275895abcc2025-08-20T03:22:00ZengElsevierClinical and Translational Radiation Oncology2405-63082025-07-015310098610.1016/j.ctro.2025.100986Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at riskVivi Tang0Elinore Wieslander1Mahnaz Haghanegi2Elisabeth Kjellén3Sara Alkner4Skåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, SwedenSkåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, SwedenSkåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, SwedenSkåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, SwedenSkåne University Hospital, Department of Hematology, Oncology and Radiation Physics, 222 42 Lund, Sweden; Lund University, Faculty of Medicine, Institute of Clinical Sciences, Department of Oncology, Barngatan 4, 22242 Lund, Sweden; Corresponding author at: Clinic of Oncology, Skåne University Hospital, 22242 Lund, Sweden.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.http://www.sciencedirect.com/science/article/pii/S2405630825000783Deep learning segmentationAI contouringTarget volume delineationDosimetric dataRadiotherapyBreast cancer |
| spellingShingle | Vivi Tang Elinore Wieslander Mahnaz Haghanegi Elisabeth Kjellén Sara Alkner Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk Clinical and Translational Radiation Oncology Deep learning segmentation AI contouring Target volume delineation Dosimetric data Radiotherapy Breast cancer |
| title | Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk |
| title_full | Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk |
| title_fullStr | Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk |
| title_full_unstemmed | Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk |
| title_short | Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk |
| title_sort | automated segmentation of target volumes in breast cancer radiotherapy impact on target size and dose to organs at risk |
| topic | Deep learning segmentation AI contouring Target volume delineation Dosimetric data Radiotherapy Breast cancer |
| url | http://www.sciencedirect.com/science/article/pii/S2405630825000783 |
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