ArticleJournal of clinical ultrasound : JCU
The Diagnostic Value of Artificial Intelligence in C-TIRADS 4-5 Nodules, Real-Time Dynamic Ultrasound and Contrast-Enhanced Ultrasound to Enhance the Difference Between Papillary Thyroid Carcinoma and Nodular Goiter.
Article in Journal of clinical ultrasound : JCU. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic value of contrast-enhanced ultrasound combined with other imaging techniques in differentiating benign and malignant thyroid nodules: a network meta-analysis.Frontiers in endocrinology · 2026Pooled it
- The Diagnostic Value of Artificial Intelligence in C-TIRADS 4-5 Nodules, Real-Time Dynamic Ultrasound and Contrast-Enhanced Ultrasound to Enhance the Difference Between Papillary Thyroid Carcinoma and Nodular Goiter.Journal of clinical ultrasound : JCUArticle
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6 authors.
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Abstract
backgroundDifferentiating papillary thyroid carcinoma (PTC) from nodular goiter (NG) in thyroid nodules is challenging. Advanced tools such as contrast-enhanced ultrasound (CEUS) and artificial intelligence (AI)-assisted diagnostics may improve diagnostic accuracy for Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) 4-5 nodules.
objectiveTo evaluate the diagnostic performance of conventional ultrasound (CUS), CEUS, and AI dynamic ultrasound in distinguishing PTC from NG in C-TIRADS 4-5 nodules.
methodsThis retrospective, single-center study included 180 PTC and 158 NG patients. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC), with statistical comparisons conducted via bootstrapping methods (1000 iterations) implemented in Python 3.12.6.
resultsThe individual models demonstrated strong diagnostic performance, with AUCs of 0.85 (C-TIRADS), 0.86 (CEUS), and 0.86 (dynamic AI). Combining models enhanced sensitivity but reduced specificity. The majority voting system, incorporating all three models, achieved the highest diagnostic performance (AUC 0.93, sensitivity 97%, specificity 89%, accuracy 93%). No significant differences were observed between AUCs due to the strong discriminatory ability of each method.
conclusionAll models, including C-TIRADS, CEUS, and dynamic AI, performed well in differentiating PTC from NG. Combining these methods, particularly with majority voting, improved diagnostic performance without compromising specificity.
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