SynthesisFrontiers in endocrinology2025
Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis.
Synthesis in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Nomogram for postoperative pathological stratification of ≥0.2 cm clinically significant lymph node metastasis burden in pN1 papillary thyroid carcinoma: development and validation using L2 ridge penalized logistic regression.Frontiers in endocrinology · 2026Article
- Machine learning-based model for predicting contralateral central lymph node metastasis in papillary thyroid carcinoma with isthmus proximity.Frontiers in endocrinology · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: This meta-analysis aims to evaluate the diagnostic performance of ultrasound (US)-based artificial intelligence (AI) in assessing cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). Methods: A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify relevant studies published up to November 19, 2024. Studies focused on the diagnostic performance of AI in the detection of CLNM of PTC were included. A bivariate random-effects model was used to calculate the pooled sensitivity and specificity, both with 95% confidence intervals (CI). The I Results: Among the 593 studies identified, 27 studies were included (involving over 23,170 patients or images). For the internal validation set, the pooled sensitivity, specificity, and AUC for detecting CLNM of PTC were 0.80 (95% CI: 0.75-0.84), 0.83 (95% CI: 0.80-0.87), and 0.89 (95% CI: 0.86-0.91), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.77 (95% CI: 0.49-0.92), 0.82 (95% CI: 0.75-0.88), and 0.86 (95% CI: 0.83-0.89), respectively. For US physicians, the overall sensitivity, specificity, and AUC for detecting CLNM were 0.51 (95% CI: 0.38-0.64), 0.84 (95% CI: 0.76-0.89), and 0.77 (95% CI: 0.73-0.81), respectively. Conclusion: US-based AI demonstrates higher diagnostic performance than US physicians. However, the high heterogeneity among studies and the limited number of externally validated studies constrain the generalizability of these findings, and further research on external validation datasets is needed to confirm the results and assess their practical clinical value. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024625725, identifier CRD42024625725.
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