ReviewReports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
A review of automatic lung tumour segmentation in the era of 4DCT.
Review in Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- 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
- 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
- Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.Journal of applied clinical medical physics · 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
- Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.PloS one · 2024Article
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
- Challenges and opportunities for artificial intelligence in oncological imaging.Clinical radiology · 2021Review
- A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.Computers in biology and medicine · 2021Review
- Roadmap: proton therapy physics and biology.Physics in medicine and biology · 2021Review
- Initial Clinical Experience of MR-Guided Radiotherapy for Non-Small Cell Lung Cancer.Frontiers in oncology · 2021Article
- Software simulation of tumour motion dose effects during flattened and unflattened ITV-based VMAT lung SBRT.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo review the literature on auto-contouring methods of lung tumour volumes on four-dimensional computed tomography (4DCT).
backgroundManual delineation of lung tumour on 4DCT has been the gold standard in clinical practice. However, it is resource intensive due to the high volume of data which results in longer contouring duration and uncertainties in defining target. Auto-contouring may present as an attractive alternative by decreasing manual inputs required, thus improving the contouring process. This review aims to assess the accuracy, variability and contouring duration of automatic contouring compared with manual contouring in lung cancer on 4DCT datasets. MATERIALS AND
methodsA search and review of literature were conducted to identify studies regarding lung tumour contouring on 4DCT. Manual and auto-contours were assessed and compared based on accuracy, variability and contouring duration.
resultsThirteen studies were included in this review and their results were compared. Accuracy of auto-contours was found to be comparable to manual contours. Auto-contouring resulted in lesser inter-observer variation when compared to manual contouring, however there was no significant reduction in intra-observer variability. Additionally, contouring duration was reduced with auto-contouring although long computation time could present as a bottleneck.
conclusionAuto-contouring is reliable and efficient, producing accurate contours with better consistency compared to manual contours. However, manual inputs would still be required both before and after auto-propagation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.