ArticleCancer management and research2021
Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network.
Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 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
- 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
- Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy.The British journal of radiology · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.Clinical Medicine Insights. Oncology · 2024Review
- Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer.Technical innovations & patient support in radiation oncology · 2023Article
- A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).Physics in medicine and biology · 2023Article
- Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective.Cancers · 2023Article
- Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review).Oncology letters · 2023Review
- Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy.JCO global oncology · 2022Review
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Authors and funding
13 authors.
Funding
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Abstract
objectiveDelineation of clinical target volume (CTV) and organs at risk (OARs) is important for radiotherapy but is time-consuming. We trained and evaluated a U-ResNet model to provide fast and consistent auto-segmentation.
methodsWe collected 160 patients' CT scans with breast cancer who underwent breast-conserving surgery (BCS) and were treated with radiotherapy. CTV and OARs were delineated manually and were used for model training. The dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to assess the performance of our model. CTV and OARs were randomly selected as ground truth (GT) masks, and artificial intelligence (AI) masks were generated by the proposed model. Two clinicians randomly compared CTV score differences of the contour. The consistency between two clinicians was tested. Time cost for auto-delineation was evaluated.
resultsThe mean DSC values of the proposed method were 0.94, 0.95, 0.94, 0.96, 0.96 and 0.93 for breast CTV, contralateral breast, heart, right lung, left lung and spinal cord, respectively. The mean 95HD values were 4.31mm, 3.59mm, 4.86mm, 3.18mm, 2.79mm and 4.37mm for the above structures, respectively. The average CTV scores for AI and GT were 2.89 versus 2.92 when evaluated by oncologist A (
conclusionOur proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.
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