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Updated: Nov 20, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Diagnostic performance of the modified Korean Thyroid Imaging Reporting and Data System for thyroid malignancy
Dong Gyu Na1, Wooyul Paik1, Jaehyung Cha2
1Department of Radiology, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea.
Purpose:
The aim of this study was to evaluate the diagnostic performance of the modified Korean Thyroid Imaging Reporting and Data System (K-TIRADS) compared with five society risk stratification systems (RSSs) according to nodule size.
Methods:
In total, 3,826 consecutive thyroid nodules (≥1 cm) with final diagnoses in 3,088 patients were classified according to five RSSs. The K-TIRADS was modified by raising the biopsy size threshold for low-suspicion nodules and subcategorizing intermediate-suspicion nodules. We assessed the performance of the RSSs as triage tests and their diagnostic accuracy according to nodule size (with a threshold of 2 cm).
Results:
Of all nodules, 3,277 (85.7%) were benign and 549 (14.3%) were malignant. In small thyroid nodules (≤2 cm), the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) had the highest reduction rate of unnecessary biopsies (76.3%) and the lowest sensitivity (76.1%). The modified K-TIRADS had the second highest reduction rate of unnecessary biopsies (67.6%) and sensitivity (86.6%). The modified K-TIRADS and ACR TI-RADS had the highest diagnostic odds ratios (P=0.165) and the highest areas under the curve (P=0.315). In large nodules (>2 cm), the sensitivity of the ACR TI-RADS for malignancy was significantly lower (88.8%) than the sensitivities of the modified K-TIRADS and other RSSs, which were very high (98.7%-99.3%) (P<0.001).
Conclusion:
The modified K-TIRADS allows a large proportion of unnecessary biopsies to be avoided, while maintaining high sensitivity and diagnostic accuracy for small malignant tumors and very high sensitivity for large malignant tumors.
