Diagnostic Accuracy of Ultrasound Radiomics for Cervical Lymph-Node Metastasis in Papillary Thyroid Carcinoma:...

Pre-operative identification of cervical lymph-node metastasis (LNM) guides surgical extent in papillary thyroid carcinoma (PTC) but remains imperfect with conventional ultrasound. To quantify the diagnostic accuracy of ultrasound-based radiomics for predicting cervical LNM in PTC and to evaluate methodological quality and standardization across published studies." Six databases were searched to 8 March 2025 (PROSPERO CRD420252XXXX). Two reviewers independently screened records, extracted data, and assessed quality with QUADAS-2 and the Radiomics Quality Score. Pooled sensitivity, specificity, diagnostic odds ratio, and hierarchical summary area under the ROC curve (AUC) were calculated using bivariate random-effects models. Subgroup/meta-regression explored nodal compartment, modelling pipeline, and validation strategy; publication bias was examined with Deeks' funnel plot and trim-and-fill. Sixty studies (10,852 patients; 4,716 with LNM) met inclusion. Radiomics-only models achieved pooled sensitivity 0.72 (95% CI: 0.66-0.78), specificity 0.81 (0.74-0.86) and AUC = 0.83 (0.79-0.86). Adding clinical variables raised sensitivity to 0.79 and AUC to 0.88, but the gain was not significant (ΔAUC = 0.05; p = 0.33). Machine-learning pipelines outperformed deep learning (AUC = 0.86 vs. 0.79), and accuracy was highest for lateral nodes (AUC = 0.94). External-validation cohorts showed lower performance (AUC = 0.79). Heterogeneity was high (I² > 80%) yet estimates were robust after bias adjustment. Most included studies originated from China, which may limit generalizability to other populations. Ultrasound radiomics provides good non-invasive accuracy for cervical nodal staging in PTC-especially for lateral compartments-though its advantage over routine clinical variables is modest. Multi-center prospective studies using standardized acquisition and reporting are needed before routine clinical adoption.