Trial reportRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2014
Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer.
Trial report in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.Journal of applied clinical medical physics · 2016Trial
- Inter-observer variability in radiotherapy contouring with the use of autocontouring software: A systematic review.Clinical and translational radiation oncology · 2026Review
- Time efficiency, geometric accuracy, and clinical impact of AI-assisted contouring of organs at risk in head and neck cancer radiotherapy.Acta oncologica (Stockholm, Sweden) · 2025Article
- Hu similarity coefficient: a clinically oriented metric to evaluate contour accuracy in radiation therapy.Scientific reports · 2024Article
- Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.Oncology letters · 2024Article
- An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.BMC medical imaging · 2024Article
- Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland.Radiation oncology journal · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
- Swin Transformer-based automatic delineation of the hippocampus by MRI in hippocampus-sparing whole-brain radiotherapy.Frontiers in neuroscience · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- A research on the improved rotational robustness for thoracic organ delineation by using joint learning of segmenting spatially-correlated organs: A U-net based comparison.Journal of applied clinical medical physics · 2023Article
- Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor.Clinical and translational radiation oncology · 2023Article
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
- A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer.Physics and imaging in radiation oncology · 2023Article
- Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?Diagnostics (Basel, Switzerland) · 2023Review
- Stress-testing pelvic autosegmentation algorithms using anatomical edge cases.Physics and imaging in radiation oncology · 2023Article
- A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy.Frontiers in oncology · 2023Article
- Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era.Journal of the National Cancer Center · 2022Review
- Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma.Journal of contemporary brachytherapy · 2022Article
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Authors and funding
14 authors.
Funding
Abstract
background and purposeTarget volumes and organs-at-risk (OARs) for radiotherapy (RT) planning are manually defined, which is a tedious and inaccurate process. We sought to assess the feasibility, time reduction, and acceptability of an atlas-based autosegmentation (AS) compared to manual segmentation (MS) of OARs. MATERIALS AND
methodsA commercial platform generated 16 OARs. Resident physicians were randomly assigned to modify AS OAR (AS+R) or to draw MS OAR followed by attending physician correction. Dice similarity coefficient (DSC) was used to measure overlap between groups compared with attending approved OARs (DSC=1 means perfect overlap). 40 cases were segmented.
resultsMean ± SD segmentation time in the AS+R group was 19.7 ± 8.0 min, compared to 28.5 ± 8.0 min in the MS cohort, amounting to a 30.9% time reduction (Wilcoxon p<0.01). For each OAR, AS DSC was statistically different from both AS+R and MS ROIs (all Steel-Dwass p<0.01) except the spinal cord and the mandible, suggesting oversight of AS/MS processes is required; AS+R and MS DSCs were non-different. AS compared to attending approved OAR DSCs varied considerably, with a chiasm mean ± SD DSC of 0.37 ± 0.32 and brainstem of 0.97 ± 0.03.
conclusionsAutosegmentation provides a time savings in head and neck regions of interest generation. However, attending physician approval remains vital.
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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.