Background: Using meta-analysis to evaluate the diagnostic value of contrast-enhanced ultrasound (CEUS) in the diagnosis of papillary thyroid microcarcinoma (PTMC). Methods: For this systematic review and meta-analysis, we searched PubMed, Cochrane Library, Web of Science, WanFang Data, VPCS Data, and China National Knowledge Infrastructure electronic databases for diagnostic studies on PTMC by CEUS from January 2013 to November 2022. Data were not available or incomplete such as case reports, nonhuman studies, etc, were excluded. Random-effects meta-analyses were used to evaluate the diagnostic accuracy of CEUS in diagnosing PTMC. The quality of the evidence was assessed with the QUADAS-2 scale. This study is registered on PROSPERO, number CRD42023409417. Results: Of 1064 records identified, 33 were eligible. The results showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CEUS in diagnosing PTMC were 0.84 (95% confidence interval [CI] = 0.83–0.86), 0.82 (95% CI = 0.80–0.83), 3.90 (95% CI = 3.23–4.72), 0.21 (95% CI = 0.18–0.25), and 20.01 (95% CI = 14.97–26.74), respectively, and the area under the summary receiver operating characteristic curve was 0.8930 (the Q index was 0.8239). The Deek funnel plot indicated publication bias (P ˂.01). Conclusion: This meta-analysis provides an overview of diagnostic accuracy of CEUS in diagnosing PTMC which indicates CEUS has a good diagnostic value for PTMC. The limitations of this study are publication bias and strong geographical bias.

Objectives To investigate the feasibility and effectiveness of a deep learning (DL) super-resolution (SR) ultrasound image reconstruction model for predicting cervical lymph node status in patients with papillary thyroid carcinoma(PTC). Methods In this retrospective study, researchers recruited 544 patients with PTC and randomly assigned them to training and test sets. SR ultrasound images were acquired using SR technology to improve image resolution, and artificial features and DL features were extracted from the original (OR) and SR images, respectively, to construct a ML, DL model. The best model was selected and aggregated with clinical parameters to construct the nomogram. The performance of the model is evaluated by ROC curves, calibration curves and decision curves. Results In distinguishing the presence or absence of metastatic lymph nodes, the predictive performance of the SR_ResNet 101 and SR_SVM models based on SR outperformed those based on OR. In the test set, SR_SVM AUC was 0.878 (95% CI 0.8203–0.9358), accuracy 0.854, while OR_SVM AUC was 0.822 (95% CI 0.7500–0.8937), accuracy 0.665. SR_ResNet 101 AUC was 0.799 (95% CI 0.7175–0.8806), accuracy 0.793, and OR_ResNet101 AUC was 0.751 (95% CI 0.6620–0.8401), accuracy 0.713. Subsequently, Nomogram_A and Nomogram_B were constructed by integrating the SR_SVM model and SR_ResNet 101 model, respectively, with clinical parameters, while Nomogram_C was constructed solely based on clinical indicators. In the test set, Nomogram_A demonstrated the best performance with an AUC of 0.930 (95% CI 0.8913–0.9682) and accuracy was 0.829. Nomogram_B AUC 0.868 (95% CI 0.8102–0.9261) and accuracy was 0.829, while Nomogram_C AUC 0.880 (95% CI 0.8257–0.9349) and accuracy was 0.787. The DeLong test revealed that the diagnostic performance of Nomogram_A based on SR_SVM was significantly higher than that of Nomogram_B, Nomogram_C, and the level of Radiologist (P < 0.05). The calibration curves and Hosmer–Lemeshow tests confirmed a high degree of fit, and the decision curve analysis demonstrated clinical value and potential patient benefit. Conclusions The predictive model constructed using SR reconstructed ultrasound images demonstrated superior performance in predicting preoperative cervical lymph node metastasis in PTC compared to OR images. The nomogram prediction model based on SR images has the potential to enhance the accuracy of predictive models and aid in clinical decision-making.