SynthesisScience progress
Diagnostic performance of the ultrasound -based artificial intelligence diagnostic system in predicting cervical lymph node metastasis in patients with thyroid cancer: A systematic review and meta-analysis.
Synthesis in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- CD44 is associated with papillary thyroid carcinoma metastasis via potential modulation of the immunosuppressive tumor microenvironment.Clinical and experimental medicine · 2026Article
- Theranostic Potentials of Metallic Nanoparticles in Thyroid Diseases: Challenges and Prospects.International journal of nanomedicine · 2026Review
- Thyroid intelligent diagnosis based on THMSNet.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BackgroundThe incidence of cervical lymph node metastasis (CLNM) in thyroid cancer (TC) is high. Accurate preoperative diagnosis of CLNM is critical to reduce unnecessary lymph node dissection and complications for TC patients. Ultrasound (US)-based artificial intelligence (AI) systems show promise for CLNM prediction, but their diagnostic performance requires systematic evaluation.MethodsA comprehensive search of four electronic databases (Web of Science, Embase, PubMed, and Cochrane Library) was conducted from inception to 30 December 2023. The random-effects model was chosen to calculate the pooled diagnostic indicators. Sensitivity analysis and heterogeneity test were conducted.ResultsAmong 19 included studies, the AI system demonstrated pooled sensitivity, specificity, area under the curve (AUC) were 0.76 (95% condidence interval (CI): 0.71-0.80), 0.78 (95% CI: 0.74-0.82), and 0.84 (95% CI: 0.15-0.99), respectively. The sensitivity, specificity and AUC in clinically node-negative (cN0) patients were 0.73 (95% CI: 0.68-0.77), 0.81 (95% CI: 0.76-0.85) and 0.83 (95% CI: 0.14-0.99). The sensitivity, specificity and AUC for the central CLNM were 0.73 (95% CI: 0.69-0.77), 0.77 (95% CI: 0.72-0.81) and 0.81 (95% CI: 0.14-0.99). Multi-center designed studies yielded higher sensitivity (0.79 vs. 0.75,
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