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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Validation of Three Scoring Risk-Stratification Models for Thyroid Nodules.

Su Min Ha¹, Hye Shin Ahn¹, Jung Hwan Baek²

¹1 Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine , Seoul, Korea.

Thyroid : Official Journal of the American Thyroid Association

|November 8, 2017

Summary

This study validated three thyroid nodule risk models. The web-based system showed the best calibration for predicting malignancy, offering simpler guidance for patient management.

Keywords:

thyroid TIRADS thyroid cancer thyroid guidelines thyroid nodules ultrasound

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Area of Science:

Radiology
Oncology
Medical Informatics

Background:

Thyroid Imaging Reporting and Data Systems (TIRADSs) were developed to reduce harm from fine-needle aspiration.
Risk stratification of thyroid nodules is crucial for appropriate management.

Purpose of the Study:

To validate three thyroid nodule risk-stratification models using ultrasonography features.
To compare a web-based system, Korean Society of Thyroid Radiology model, and American College of Radiology model.

Main Methods:

Ultrasonography features (content, echogenicity, shape, margin, calcifications) of 1112 thyroid nodules from 954 patients were assessed.
Discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC).
Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.

Main Results:

Thyroid malignancy was confirmed in 37.2% of nodules.
All three models demonstrated acceptable predictive accuracy with similar AUC values (0.875-0.891).
The web-based scoring system exhibited the best calibration (p=0.078).

Conclusions:

Ultrasonography-based risk-stratification models for thyroid nodules show comparable predictive accuracy.
The web-based scoring system offers superior calibration and may simplify clinical decision-making.
An accessible web-based system can streamline personalized management of thyroid nodules.