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

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, p < 0.01) and specificity (0.79 vs. 0.78, p < 0.01) than single-center designs. Deep learning (DL) yielded higher sensitivity (0.79 vs. 0.74, p < 0.01) and specificity (0.83 vs. 0.75, p < 0.01) than classic machine learning. Studies published after 2022 yielded higher sensitivity (0.77 vs. 0.74, p < 0.01) than before 2022. Studies from China had lower specificity than studies from other countries (0.78 vs. 0.80, p = 0.01). Models incorporating multimodal features outperformed unimodal US (specificity: 0.79 vs. 0.75, p < 0.01).ConclusionUS-based AI systems exhibit favorable predictive value for CLNM in TC, particularly with DL and multimodal designs, potentially reducing overtreatment. Prospective validation is needed prior to clinical adoption.

Keywords: Artificial intelligence; lymph node metastasis; meta-analysis; systematic review; thyroid cancer; ultrasound.

Figure 1.

Flow diagram of the preferred reporting items for systematic reviews and meta-analyses (PRISMA).

Figure 2.

Diagnostic performance of US-based AI diagnostic system for CLNM prediction. (a) SROC curve analysis showing pooled AUC with 95% CI; (b) Forest plots demonstrating a high degree of heterogeneity in sensitivity and specificity across included studies. CLNM: cervical lymph node metastasis; SROC: summary receiver operating characteristic curve; AUC: area under the curve; CI: confidence interval.

Figure 3.

Diagnostic performance of US-based AI diagnostic system for CLNM prediction in TC patients with cN0. (a) SROC curve analysis showing pooled AUC with 95% CI; (b) forest plots demonstrating a high degree of heterogeneity in sensitivity and specificity across included studies. CLNM: cervical lymph node metastasis; TC: thyroid cancer; SROC: summary receiver operating characteristic curve; AUC: area under the curve; CI: confidence interval; cN0: clinically node-negative.

Figure 4.

Diagnostic performance of US-based AI diagnostic system for central CLNM prediction. (a) SROC curve analysis showing pooled AUC with 95% CI; (b) forest plots demonstrating a high degree of heterogeneity in sensitivity and specificity across included studies. US: ultrasound; AI: artificial intelligence; CLNM: cervical lymph node metastasis; SROC: summary receiver operating characteristic curve; AUC: area under the curve; CI: confidence interval.

Figure 5.

Sensitivity analysis results of the included studies. (a) Goodness of fit; (b) bivariate normality; (c) influence analysis; (d) outlier detection.

Figure 6.

US-based AI for CLNM risk assessment. (a) Fagan nomogram: 50% pre-test probability converts to 78% (LR+ 3) or 24% (LR− 0.31) post-test probability; (b) LR classification matrix with evidence thresholds (strong: LR+＞10). US: ultrasound; AI: artificial intelligence; CLNM: cervical lymph node metastasis; LR: likelihood ratio.