ArticlePhysics and imaging in radiation oncology2025
Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- HIF1-driven TDP-43 stabilizes TRIP6 mRNA to drive angiogenesis and colorectal cancer progression under hypoxia.Translational oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Background and purpose: Tumor hypoxia is linked to lower local control rates and increased distant disease progression during head and neck (HN) radiotherapy. Materials and methods: One hundred and thirty-four (training = 84, validation = 13, testing = 21, additional testing = 16) HN carcinoma patients, treated with chemoradiotherapy between 2011 and 2018 and scanned at treatment baseline with Results: The AI model hypoxic volume predictions were well-correlated with Conclusion: Voxel-wise prediction of hypoxia for HN cancers from a 2D deep learning model using FDG-PET images as inputs was shown to be feasible. Testing on larger institutional and multi-institutional cohorts is required to establish generalizability.
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Registered trials
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