ArticleTechnical innovations & patient support in radiation oncology2019
Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy.
Article in Technical innovations & patient support in radiation oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed.
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Target Contour Consistency During Magnetic Resonance-Guided Online Adaptive Stereotactic Body Radiation Therapy.Advances in radiation oncology · 2025Article
- Impact of bowel space contouring variability on radiation dose and volume assessments in treatment planning for gynaecologic cancers.Journal of medical radiation sciences · 2023Article
- Deep Learning Architecture to Improve Edge Accuracy of Auto-Contouring for Head and Neck Radiotherapy.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical validation of a semi-automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions.Journal of medical radiation sciences · 2023Article
- The Impact of Interactive MRI-Based Radiologist Review on Radiotherapy Target Volume Delineation in Head and Neck Cancer.AJNR. American journal of neuroradiology · 2023Article
- Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes.Thoracic cancer · 2022Article
- Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans.Physics and imaging in radiation oncology · 2022Article
- Dose-volume-based evaluation of convolutional neural network-based auto-segmentation of thoracic organs at risk.Physics and imaging in radiation oncology · 2022Article
- Recognising the challenges of implementing multi-centre adaptive plan of the day radiotherapy.Technical innovations & patient support in radiation oncology · 2022Article
- The quantitative impact of joint peer review with a specialist radiologist in head and neck cancer radiotherapy planning.The British journal of radiology · 2022Article
- Challenges in the target volume definition of lung cancer radiotherapy.Translational lung cancer research · 2021Review
- Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours.Frontiers in oncology · 2021Article
- Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients.PloS one · 2021Article
- Contrast enhanced oesophageal avoidance for stereotactic body radiotherapy: Barium vs. Gastrografin.Technical innovations & patient support in radiation oncology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis study aimed to examine whether any significant differences existed in trial protocol compliance in target volumes (TV) and organs at risk (OARs) contouring amongst clinical oncologists specialised in lung cancer radiotherapy. MATERIALS/
methodsTwo lung radiotherapy trials that require all prospective investigators to submit pre-trial outlining quality assurance (QA) benchmark cases were selected. The contours from the benchmark cases were compared against a set of reference contours which were defined by the trial management group (TMG). In order to quantify the degree of variation in TV and OARs contouring, the matching index (MI), Dice coefficient (DICE), Jaccard index (JI), Van't Riet Index and geographical miss index (GMI) were calculated.
resultsA total of 198 structures contoured by 21 clinicians were collected from the outlining benchmark cases. There were 40 clinical target volumes (CTV), 32 spinal cord, 36 oesophagus, 36 heart and 54 lungs volumes included in the study. Analysis of the pre-trial benchmark cases revealed statistically significant differences (p ≤ 0.05) in trial protocol compliances between clinical oncologists' target volume and organs at risk contours. Our results demonstrated that the lung contours had the highest level of conformity, followed by heart, CTV, spinal cord and oesophagus respectively.
conclusionsThis study showed that there was a statistically significant difference in trial protocol compliance for lung clinical oncologists' TV and OARs contouring within the pre-trial QA benchmark cases. Trial protocol compliances of TV and OARs delineation can be identified through assessing outlining QA benchmark cases.
Indexed as
Identifiers
What OpenQuestion holds
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.