ReviewTranslational lung cancer research2021
Challenges in the target volume definition of lung cancer radiotherapy.
Review in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Clinical implementation of automated IMRT/VMAT planning for lung cancer radiation therapy using the expedited constrained hierarchical optimization (ECHO) system.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Monitoring lung tumour volume on daily cone beam CT; is it achievable in a real-world setting?Technical innovations & patient support in radiation oncology · 2025Article
- Mathematical mechanistic model representing the cancer immunity cycle under radiation effects.Scientific reports · 2025Article
- Article
- EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy.Cancers · 2024Article
- A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens.Diagnostics (Basel, Switzerland) · 2024Article
- Lung cancer and obesity: A contentious relationship (Review).Oncology reports · 2024Review
- Optimizing target and diaphragmatic configuration, and dosimetric benefits using continuous positive airway pressure in stereotactic ablative radiotherapy for lung tumors.Radiation oncology journal · 2024Article
- Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications.Physics and imaging in radiation oncology · 2024Article
- Multitask connected U-Net: automatic lung cancer segmentation from CT images using PET knowledge guidance.Frontiers in artificial intelligence · 2024Article
- Validation of deep learning-based CT image reconstruction for treatment planning.Scientific reports · 2023Article
- The spine and carina as a surrogate for target registration in cone-beam CT imaging verification in locally advanced lung cancer radiotherapy.Radiology and oncology · 2023Article
- Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon.Journal of clinical medicine · 2022Article
- Thorax radiotherapy usingFrontiers in oncology · 2022Article
- Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors.Journal of medical physicsArticle
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.
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