ArticleQuantitative imaging in medicine and surgery2024
Comparison of diagnostic performance of the current score-based ultrasound risk stratification systems according to thyroid nodule size.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Article
- Development and validation of a nomogram based on conventional and contrast-enhanced ultrasound for differentiating malignant from benign thyroid nodules.Quantitative imaging in medicine and surgery · 2025Article
- Optimizing C-TIRADS for sub-centimeter thyroid nodules using machine learning-derived feature importance.Frontiers in endocrinology · 2025Article
- LLM evaluation for thyroid nodule assessment: comparing ACR-TIRADS, C-TIRADS, and clinician-AI trust gap.Frontiers in endocrinology · 2025Article
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6 authors.
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Abstract
Background: The lack of standardization in risk stratification systems (RSSs) has led to uncertainty in selecting the most effective RSS for diagnosing malignancy risk in thyroid nodules. Therefore, the aim of this study was to compare the diagnostic performance of four current score-based RSSs according to thyroid nodule size, with the goal of determining the most effective RSS and aiding in clinical decision-making. Methods: Between July 2013 and January 2019, a total of 2,667 consecutive patients presenting with 3,944 thyroid nodules were pathologically diagnosed after thyroidectomy and/or ultrasound (US)-guided fine-needle aspiration (FNA). These nodules were retrospectively dichotomized into two groups: small nodules (<1 cm) and large nodules (≥1 cm). The four RSSs were used to assign US categories, and the diagnostic performances were computed and compared based on the size of thyroid nodules, both before and after the application of size thresholds for biopsy. Results: After thyroidectomy or biopsy, 1,781 (45.2%) thyroid nodules were found to be malignant. (I) After applying size thresholds for biopsy in ≥1 cm nodules, the highest specificity, accuracy, area under the curve (AUC) and the lowest FNA rate and unnecessary FNA rate were observed in the Artificial Intelligence-Thyroid Imaging Reporting And Data System (AI-TIRADS) (66.1%, 75.3%, 0.785, 55.1%, and 38.6%, respectively, P<0.05 for all). (II) Before applying size thresholds for biopsy in ≥1 cm nodules, the FNA rate and unnecessary FNA rate of the four RSSs were lower they were after the application of the size threshold: American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), 59.1% versus 61.4%, 39.8% versus 45.4%; AI-TIRADS, 52.3% versus 55.1%, 34.0% versus 38.6%; TIRADS issued by Kwak Conclusions: A potential effective strategy for managing large nodules in the current score-based RSSs could be to rely solely on US categories rather than size thresholds for biopsy. Additionally, the diagnostic performance of small nodules showed higher sensitivity and lower specificity compared to large nodules before applying size thresholds for biopsy. These findings suggest a possible new management strategy for large nodules and provide a basis for the managing small nodules.
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