ArticleFrontiers in oncology2024
Application of ultrasound elastography and radiomic for predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction model for cervical lymph node metastasis of papillary thyroid microcarcinoma: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Development of a Machine Learning-Based Predictive Model for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.Cancer medicine · 2026Article
- Noninvasive immune-inflammatory profiling by ultrasound radiomics for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.Frontiers in immunology · 2026Article
- Personalized prediction of lymph node metastasis in papillary thyroid microcarcinoma: a nomogram and web calculator.Scientific reports · 2025Article
- Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2025Article
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6 authors.
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Abstract
Objective: This study aims to combine ultrasound (US) elastography (USE) and radiomic to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: A total of 204 patients with 204 thyroid nodules who were confirmed with PTMC and treated in our hospital were enrolled and randomly assigned to the training set (n = 142) and the validation set (n = 62). US features, USE (gender, shape, echogenic foci, thyroid imaging reporting and data system (TIRADS) category, and elasticity score), and radiomic signature were employed to build three models. A nomogram was plotted for the combined model, and decision curve analysis was applied for clinical use. Results: The combined model (USE and radiomic) showed optimal diagnostic performance in both training (AUC = 0.868) and validation sets (AUC = 0.857), outperforming other models. Conclusion: The combined model based on USE and radiomic showed a superior performance in the prediction of CLNM of patients with PTMC, covering the shortage of low specificity of conventional US in detecting CLNM.
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