Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis

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: statistic was used to assess heterogeneity among studies. 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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