ArticleJournal of cancer research and clinical oncology2026
Comment on "Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes".
Article in Journal of cancer research and clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Xue and Chen et al.'s review connects AI-derived CT features of lung ground-glass nodule adenocarcinoma to driver gene status. Three aspects are missing. There is no stated search strategy no databases, date range, or inclusion criteria despite drawing on more than 150 references, so readers cannot tell whether weaker or null results were sought out or just left aside. Individual studies are cited by their best accuracy or AUC figures, from approximately 0.64 to above 0.95, with no attempt to weigh them by sample size, design, or validation status. The framing does not match the evidence the review cites: the text calls AI transformative, but a systematic review it references reports a mean AUC of only ~0.64 for driver gene prediction from imaging. None of this undercuts the review's usefulness as a starting map of the field, but a stated search process, some quality-weighting of the cited numbers, and framing that matches the aggregate evidence would make it more reliable.
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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.