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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Thyroid Image Reporting and Data System Categorization: Effectiveness in Pediatric Thyroid Nodule Assessment.
Cigdem Uner1, Sonay Aydin, Berna Ucan
1Department of Radiology, Dr Sami Ulus Training and Research Hospital, Ankara, Turkey.
Ultrasound Quarterly
|November 15, 2019
Summary
The Thyroid Image Reporting and Data System (TI-RADS) effectively assesses pediatric thyroid nodules. Categories 4 and 5 indicate the highest malignancy risk, warranting pathological evaluation.
Area of Science:
- Pediatric Endocrinology
- Radiology
- Oncology
Background:
- Thyroid nodules are less common in children but carry a significant malignancy risk (approx. 25%).
- Thyroid Image Reporting and Data System (TI-RADS) validation in pediatric cases is limited, despite its use in adults.
Purpose of the Study:
- To evaluate the diagnostic performance of the TI-RADS risk stratification method for pediatric thyroid nodules.
- To determine the efficacy of TI-RADS in predicting malignancy in pediatric thyroid nodules.
Main Methods:
- Retrospective analysis of 68 nodules from 64 pediatric patients (aged ≥18 years) with pathological diagnoses.
- Evaluation of ultrasonography (US) images or reports to assign TI-RADS scores and categories.
- Statistical analysis including Area Under the Curve (AUC) estimation.
Main Results:
- The study included 68 nodules (48 benign, 20 malignant) from 64 patients (mean age 15.15 years).
- TI-RADS categorization demonstrated significant diagnostic efficacy with an AUC of 0.89 (95% CI: 0.80-0.98).
- TI-RADS categories 4 and 5 were identified as optimal cutoffs for predicting malignancy.
Conclusions:
- TI-RADS is an effective tool for assessing pediatric thyroid nodules.
- Pediatric thyroid nodules classified as TI-RADS 4 or 5 exhibit the highest risk of malignancy and require pathological examination.
