A prospective randomized evaluation of AI assisted GTV contouring in head-and-neck radiotherapy
In brief
AI-assisted tumor contouring shows no meaningful benefit in head-and-neck radiotherapy
In this randomized study of 83 patients, AI-assisted and manual contouring needed similar amounts of editing for primary tumors; lymph-node contours tended to need more editing with AI on one measure, but not others. Conference duration and contouring time were also similar, so the study found no efficiency gain and leaves the value of wider adoption uncertain.
- Journal
- Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology (Q1)
- Published
- 1 November 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Mathis E Rasmussen, Jintao Ren, Jasper A Nijkamp, Anne I S Holm, Kristoffer Moos, Kim M Hochreuter, et al.
- PMID
- 42967540
- DOI
- 10.1016/j.radonc.2026.111854
Why clinicians should know about it
- Picked for Radiation Oncology (top studies of the week, 8 November 2026): Randomized evaluation of GTV contouring for head-and-neck radiotherapy
- Picked for Radiology, Radiation Oncology, Nuclear Medicine, Medical Physics and Imaging (top studies of the week, 8 November 2026): Randomized clinical evaluation of AI-assisted radiotherapy target contouring
Abstract
BACKGROUND: AI-assisted contouring has demonstrated efficiency and consistency benefits for organs-at-risk in radiotherapy. However, its impact on gross tumor volume (GTV) delineation remains unexplored. The aim of this study was to evaluate AI-assisted GTV contouring in head and neck cancer radiotherapy in an exploratory randomized controlled clinical trial. METHODS: In this prospective, single-centre, randomized controlled evaluation, patients referred for curative-intent radiotherapy of head-neck cancer were randomized to manual contouring (control) or AI-assisted contouring (intervention) of gross tumor volumes using an in-house developed segmentation model. The primary endpoint was the extent of editing during a blinded multidisciplinary target conference, using normalized Added Path Length (APL). Secondary endpoints included conference duration, self-reported contouring time, and perceived complexity of the cases. RESULTS: Between Sept 11, 2023, and Jan 20, 2025, 85 patients were enrolled; 83 were included in the analysis (41 control, 42 AI-assisted). The median difference in APL (AI-assisted minus control, negative favoring the intervention) was -0.02 [-0.32; 0.07] for GTV-T and 0.06 [-0.01; 0.14] for GTV-N. Conference duration, contouring time, and perceived complexity were similar between groups. No adverse events were reported. CONCLUSION: The median difference in APL between groups was consistent with no relevant effect for GTV-T. For GTV-N, however, AI-assisted contouring tended towards more editing during peer review for APL, but not for supplementary contour metrics. No efficiency benefits were demonstrated. Overall, these findings do not indicate a concern, but warrants further research before widespread adoption.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.