ArticlePhysics and imaging in radiation oncology2024
Artificial intelligence-generated targets and inter-observer variation in online adaptive radiotherapy of bladder cancer.
Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Inter-observer variability in radiotherapy contouring with the use of autocontouring software: A systematic review.Clinical and translational radiation oncology · 2026Review
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- Clinical implementation of RTT-only CBCT-guided online adaptive focal radiotherapy for bladder cancer.Clinical and translational radiation oncology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Daily target re-delineation in online adaptive radiotherapy (oART) introduces uncertainty. The aim of this study was to evaluate artificial intelligence (AI) generated contours and inter-observer target variation among radiotherapy technicians in cone-beam CT (CBCT) guided oART of bladder cancer. Materials and methods: For each of 10 consecutive patients treated with oART for bladder cancer, one CBCT was randomly selected and retrospectively included. The bladder (CTV-T) was AI-segmented (CTV-T Results: CTV-T Conclusions: Target re-delineation in CBCT-guided oART of bladder cancer demonstrated non-isotropic inter-observer variation. Manual adjustment of AI-generated contours was necessary to cover ground truth targets.
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Registered trials
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