Improving the Precision of Deep-Learning-Based Head and Neck Target Auto-Segmentation by Leveraging Radiology Reports Using a Large Language Model.

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

3 citing papers in PubMed.

1. Evaluation of AI-derived LAD artery dose metrics for survival stratification in stage III NSCLC: A secondary analysis of RTOG 0617 trial.Medical physics · 2026
   Trial
2. [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
   Review
3. Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

7 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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