ArticleRadiation oncology (London, England)2021
Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- The illusory correlation between geometrical similarity and physician satisfaction in heart auto-segmentation revealed by a physician-blind test.Journal of applied clinical medical physics · 2026Article
- Clinical safety and breast symmetry after injectable acellular dermal fibers reconstruction: an MR-based volumetric study.Gland surgery · 2026Article
- Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance.The British journal of radiology · 2026Article
- Performance of a Vendor System for Systematic Offline Adaptive Breast Helical Radiotherapy.Cancers · 2026Article
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 2026Article
- Quality assurance incorporating artificial intelligence-generated reference contours in a phase II radiotherapy trial.Physics and imaging in radiation oncology · 2026Article
- Quantitative assessment of inter-observer consistency in target volume delineation following breast-conserving surgery and mastectomy.Frontiers in oncology · 2026Article
- Automated segmentation of target volumes in breast cancer radiotherapy, impact on target size and dose to organs at risk.Clinical and translational radiation oncology · 2025Article
- A semi-automated workflow for cohort-wise preparation of radiotherapy data for dose-response modeling, including autosegmentation of organs at risk.Journal of applied clinical medical physics · 2025Article
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation.Clinical and translational radiation oncology · 2024Article
- Article
- LLM-driven multimodal target volume contouring in radiation oncology.Nature communications · 2024Article
- The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck.Journal of personalized medicine · 2024Article
- Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy.Acta oncologica (Stockholm, Sweden) · 2024Article
- Accurate object localization facilitates automatic esophagus segmentation in deep learning.Radiation oncology (London, England) · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Quantitative assessment of breast volume changes after whole-breast irradiation for breast cancer using breast auto-segmentation.Breast cancer research and treatment · 2024Article
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Abstract
backgroundIn breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients.
methodsCTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data.
resultsThe correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0-10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal.
conclusionsThe feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.
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