ArticleThoracic cancer2022
Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes.
Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 9 citations in OpenAlex.
- Deep learning-based independent lymph node segmentation in esophageal cancer: a precise and efficient approach for radiotherapy planning.Quantitative imaging in medicine and surgery · 2026Article
- Automated contouring of gross tumor volume lymph nodes in lung cancer by deep learning.BMC cancer · 2025Article
- Physician-guided deep learning model for assessing thymic epithelial tumor volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial intelligence in automatic image segmentation system for exploring recurrence patterns in small cell carcinoma of the lung.Frontiers in oncology · 2025Article
- Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk.Physics and imaging in radiation oncology · 2025Review
- Article
- Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review).Oncology letters · 2023Review
- Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes.Thoracic cancer · 2022Article
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map.
methodsComputed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted.
resultsThe mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV.
conclusionThis is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.
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