ArticleRadiation oncology (London, England)2021
Interobserver variability in organ at risk delineation in head and neck cancer.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Malnutrition and radiation therapy in head and neck cancers, a systematic review on reported definitions and associated factors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Pooled it
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- Inverse treatment planning using deep learning-based organs at risk in radiotherapy for head and neck cancer: a prospective planning study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Tumor Growth Rate in Head and Neck Squamous Cell Carcinoma: A Scoping Review.Laryngoscope investigative otolaryngology · 2026Review
- A multi-centre real-world evaluation of AI-assisted organ at risk contouring on radiotherapy treatment planning workflows.The British journal of radiology · 2026Article
- Inter-center variability of dosimetry methodology and the impact on reported absorbed dose for [European journal of nuclear medicine and molecular imaging · 2026Article
- Evaluation of a quasi-automatic treatment planning workflow for head-and-neck cancer in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- Interobserver Variation Within Planning Target Volume and Organs at Risk in a Patient with Oropharyngeal Carcinoma: A Contouring Study with Anatomical Analysis.Current oncology (Toronto, Ont.) · 2026Article
- Dynamic blood dose estimates in radiotherapy and correlations with adverse clinical outcomes in brain, head-and-neck, and lung cancer patients.Journal of applied clinical medical physics · 2026Article
- Impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: a SIOP-RTSG radiotherapy committee study.Clinical and translational radiation oncology · 2026Article
- Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Three discipline collaborative radiation therapy (3DCRT) special debate: AI structure segmentation is better than clinician contouring for both OARs and targets.Journal of applied clinical medical physics · 2025Article
- Clinical feasibility of atlas-based auto-segmentation for organ-at-risk contouring in head-and-neck radiotherapy.Journal of applied clinical medical physics · 2025Article
- A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.Physics and imaging in radiation oncology · 2025Article
- 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
- Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.NPJ digital medicine · 2025Article
- Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients.Cancers · 2025Article
- Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.Journal of applied clinical medical physics · 2025Article
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
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5 authors.
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Abstract
backgroundIn radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines.
methodsHNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison.
resultsFourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible [median DSC 0.9, range (0.8-0.9); median MSD 1.1 mm, range (0.8-8.3), median HD95 3.4 mm, range (1.5-38.7)], brainstem [median DSC 0.9 (0.6-0.9); median MSD 1.5 mm (1.1-4.0), median HD95 4.0 mm (2.3-15.0)], submandibular glands [median DSC 0.8 (0.5-0.9); median MSD 1.2 mm (0.9-2.5), median HD95 3.1 mm (1.8-12.2)] and parotids [median DSC 0.9 (0.6-0.9); median MSD 1.9 mm (1.2-4.2), median HD95 5.1 mm (3.1-19.2)]. Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p = 0.008).
conclusionsAlthough ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.
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