SynthesisFrontiers in medicine2026
Diagnostic accuracy of CT-based radiomics for predicting occult lymph node metastasis in early-stage lung adenocarcinoma: a systematic review and meta-analysis.
Synthesis in Frontiers in medicine, 2026. 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.
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to evaluate the diagnostic accuracy of CT-based radiomics for predicting occult lymph node metastasis (OLNM) in early-stage lung adenocarcinoma. Methods: Relevant studies up to December 2025 were systematically searched in the databases of PubMed, Embase, Cochrane Library, and Web of Science. Diagnostic accuracy was assessed by pooled estimates of sensitivity, specificity, likelihood ratios, the diagnostic odds ratio, and the summary receiver operating characteristic curve. Subgroup analyses was then conducted to determine sources of heterogeneity. Results: This study evaluated 10 articles and 6,349 patients. By meta-analysis, CT-based radiomics demonstrated good diagnostic performance for OLNM. The pooled sensitivity and specificity were 0.85 and 0.78 in internal validation cohorts and 0.72 and 0.75 in external validation cohorts, respectively. The area under the summary receiver operating characteristic curve was 0.89 and 0.80 for internal and external validation, respectively. Subgroup analyses of the external validation cohorts suggested possible variation in diagnostic performance according to sample size, CT protocol, and segmentation method. Conclusion: CT-based radiomics shows potential for non-invasive prediction of OLNM in early-stage lung adenocarcinoma. Further multicenter prospective studies with harmonized imaging and radiomics pipelines are needed to confirm its clinical applicability. Systematic review registration: CRD420261299869.
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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 Socratic graph.