ArticleThe British journal of radiology2025
Real-world performance evaluation of commercial autocontouring software for head and neck cancer radiotherapy.
Article in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Artificial intelligence in neuro-oncology: Current advances and legal considerations in the European Union.Neuro-oncology advancesReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo comprehensively evaluate the ability of ART-Plan (autocontouring software using artificial intelligence [AI]) to contour head and neck cancer (HNC) radiotherapy structures (organs-at-risk and elective nodal volumes) in a real-world setting, as required by NICE guidelines.
methodsRetrospective evaluation (n = 60) compared clinically used contours to AI-contours, using volumetric dice similarity coefficient (VDSC) and blinded radiation oncologist (RO) contour preference assessment. AI-contours were then generated prospectively for HNC radiotherapy patients (n = 61), before review by ROs. ROs recorded qualitative scoring and review/editing time. Geometric and dosimetric comparison of AI-contours and final contours was undertaken. Correlation coefficients between all metrics were calculated.
resultsRetrospective median VDSC varied widely for different structures (0.23-0.88). 31.4% blinded contour assessments preferred clinician-generated contours, 32.9% preferred AI-generated contours, 35.7% saw no significant difference. Prospective evaluation showed AI-contour yielded significant time-saving in reviewing/editing for all structures compared to manual contouring. Qualitative scores demonstrated most structures had median scoring ≥4 (indicating no editing required). Geometric metrics showed high similarity for all structures except larynx. Dosimetric evaluation demonstrated clinically significant dose differences for larynx and elective nodal volumes. Strong correlation was seen between qualitative scoring and all geometric metrics.
conclusionsAI-contours showed excellent qualitative performance and facilitated time-saving. Protocol differences exist between commercial AI-solutions and implementing centres. Ongoing final clinician review of AI-contours remains essential. ADVANCES IN KNOWLEDGE: This is the first study demonstrating ART-Plan's capability for excellent qualitative ratings and significant time-savings for HNC radiotherapy contouring in a real-world workflow setting. 5-point Likert-scale qualitative scoring correlates strongly with geometric metrics.
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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.