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Updated: Jun 3, 2026

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

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

Published on: April 21, 2023

Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using

Yang Li¹, Xing Yan¹, Jiao Li¹

¹Department of Ultrasound Diagnosis, The Second Xiangya Hospital, Central South University, Changsha, China; Research Center of Ultrasonography, The Second Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center for Ultrasound Diagnosis and Treatment in Hunan Province, Changsha, China.

Ultrasound in Medicine & Biology

|June 1, 2026

Summary

This summary is machine-generated.

A new machine-learning model integrating contrast-enhanced ultrasound (CEUS) and conventional ultrasound (US) radiomics effectively distinguishes papillary thyroid carcinomas (PTCs) from mummified thyroid nodules (MTNs). Clinical features did not enhance diagnostic performance, highlighting the importance of appropriate modality combination.

Keywords:

Contrast-enhanced ultrasound Mummified thyroid nodule Papillary thyroid carcinoma Radiomics

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Distinguishing mummified thyroid nodules (MTNs) from papillary thyroid carcinomas (PTCs) is crucial for appropriate patient management.
Conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) provide complementary information for thyroid nodule characterization.
Radiomics analysis of US and CEUS images offers potential for quantitative feature extraction and improved diagnostic accuracy.

Purpose of the Study:

To develop and validate a machine-learning model integrating US radiomics, CEUS radiomics, and clinical features for differentiating MTNs from PTCs.
To assess the diagnostic performance and clinical utility of the developed radiomics model.
To provide insights into optimal modality combinations for thyroid nodule classification.

Main Methods:

A retrospective study included 120 PTCs and 84 MTNs.
Radiomics features were extracted from conventional US and CEUS images.
Machine learning models, including Logistic Regression and Support Vector Machine, were trained and validated.
Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Main Results:

The combined US+CEUS radiomics model achieved an area under the curve (AUC) of 0.936 in the training set and 0.881 in the test set.
The model, primarily driven by CEUS features, demonstrated superior diagnostic performance compared to models using only US or clinical data.
Decision curve analysis indicated significant clinical utility of the US+CEUS radiomics model.

Conclusions:

The integrated US+CEUS radiomics model exhibits high diagnostic value for differentiating PTCs from MTNs.
CEUS radiomics features play a dominant role in the model's performance.
Combining clinical features with radiomics did not significantly improve diagnostic accuracy, suggesting careful consideration of modality integration based on disease characteristics.