ArticleClinical and translational radiation oncology2022
Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients.
Article in Clinical and translational radiation oncology, 2022. 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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- 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
- Longitudinal assessment of diffusion-weighted imaging during magnetic resonance-guided radiotherapy in head and neck cancer.Radiation oncology (London, England) · 2025Trial
- MRI-based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.Precision radiation oncology · 2026Review
- 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
- Eliminating the high-risk CTV1 margin in DAHANCA oropharyngeal radiotherapy: Dosimetric impact on dysphagia and organ-at-risk doses.Acta oncologica (Stockholm, Sweden) · 2025Article
- Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.Radiation oncology (London, England) · 2025Article
- Treatment of Non-Hodgkin Lymphoma Involving Head and Neck Sites with a 1.5 T MR-Linac: Preliminary Results from a Prospective Observational Study.Hematology reports · 2025Article
- Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Improvement in male pelvis magnetic resonance image contouring following radiologist-delivered training.Journal of medical radiation sciences · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
- Deep Learning Architecture to Improve Edge Accuracy of Auto-Contouring for Head and Neck Radiotherapy.Diagnostics (Basel, Switzerland) · 2023Article
- Article
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC. Material/methods: 32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD). Results: 32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08-0.39 cm) and 0.07 cm (range 0.04-0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001). Conclusion: Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.
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