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Updated: Sep 10, 2025

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
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A Novel Scoring System for AUS Thyroid Nodule.
Nuha A Alsaleh1, Mohammed A Alswayyed2, Shatha A Alduraywish3
1Department of Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
International Journal of Endocrinology
|August 21, 2025
Summary
This study subclassified Bethesda III atypia of undetermined significance (AUS) thyroid nodules, revealing varied malignancy risks. A novel scoring system aids in assessing malignancy risk for better thyroid nodule management.
Area of Science:
- Endocrinology
- Cytopathology
- Oncology
Background:
- Thyroid nodules classified as Bethesda III atypia of undetermined significance (AUS) present diagnostic challenges.
- Accurate risk stratification is crucial for appropriate patient management and avoiding unnecessary procedures.
Purpose of the Study:
- To subclassify Bethesda III AUS thyroid lesions using fine-needle aspiration cytology (FNAC).
- To develop and validate a novel scoring system for estimating malignancy risk in Bethesda III subcategories.
Main Methods:
- Retrospective analysis of 338 thyroidectomy cases with Bethesda III FNAC findings.
- Subclassification of Bethesda III into six subtypes (III-A to III-F).
- Development of a scoring system integrating cytological, radiological, and clinical factors.
Main Results:
- Malignancy rates varied significantly across subcategories, with III-B showing the highest (54.73%).
- Cytological subcategory and high-risk ultrasound features were significantly associated with malignancy (p < 0.05).
- The proposed scoring system effectively stratified patients into low, moderate, and high malignancy risk groups.
Conclusions:
- Subclassification of Bethesda III AUS nodules improves malignancy risk assessment.
- The novel scoring system offers a practical tool for guiding individualized thyroid nodule management.
- Further prospective studies are needed to validate the scoring system's predictive value.
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