SynthesisScientific reports2025
Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Assessment and pathways of the energy production revolution in the Yellow River Basin, China towards carbon peaking: a machine learning approach.Scientific reports · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate dose distribution prediction in head and neck radiotherapy remains complex due to anatomical variability and proximity of critical organs. This study presents a meta-analysis of convolutional neural networks (CNNs) and related architectures to evaluate their predictive efficacy. The MEDLINE (PubMed), Embase (Elsevier and Ovid), Scopus, and Scholar databases were searched using a structured search string. EndNote software was used for screening. Meta-analysis was conducted using the effect size of the absolute prediction error (MAE) for D95 in the planning target volume (PTV) and Dmax in the spinal cord (as a representative organ at risk, OAR). The PROBAST tool was used to assess methodological quality. Missing training parameters were retrieved from supplementary sources, and updated analyses were performed. For PTV (D95), advanced CNNs achieved the highest pooled accuracy (MAE: 0.95, 95% CI: 0.62-1.27), though substantial heterogeneity (I
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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.