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Updated: Jan 7, 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

CT-based explainable machine learning for predicting benign and malignant thyroid nodules: a multi-center study.

Haijun He¹, Mingquan Luo¹, Kai Hu¹

¹Department of Radiology, Nanbu County People's Hospital, Nanchong, Sichuan, China.

Frontiers in Oncology

|December 25, 2025

Summary

This summary is machine-generated.

This study developed an explainable machine learning model for predicting thyroid nodule malignancy using CT scans. The combined model accurately differentiates benign from malignant nodules, aiding clinical decisions.

Keywords:

CT SHapley additive explanations benignity or malignancy machine learning thyroid nodules

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Thyroid nodules are common, with malignancy prediction posing a clinical challenge.
Accurate preoperative differentiation is crucial for appropriate patient management.

Purpose of the Study:

To develop and validate a CT-based explainable machine learning model for predicting thyroid nodule malignancy.
To improve the accuracy of preoperative diagnosis and guide clinical decision-making.

Main Methods:

Retrospective analysis of 370 thyroid nodules with pathological confirmation.
Extraction of radiomics features from CT images to create a radiomics score (Rad_Score).
Development of clinical, imaging, and combined (clinical + Rad_Score) models using logistic regression (LR) and support vector machine (SVM) algorithms.
SHAP analysis for model interpretability.

Main Results:

The LR combined model achieved high AUC values (0.962 training, 0.913 internal validation, 0.914 external validation).
The combined model outperformed individual clinical and radiomics models.
SHAP analysis highlighted the importance of Rad_Score, enhancing model transparency.

Conclusions:

The CT-based, explainable combined model using LR demonstrates superior performance for preoperative thyroid nodule malignancy prediction.
This non-invasive tool offers an efficient and transparent method for differentiating benign from malignant nodules.
The model has the potential to optimize individualized clinical management strategies for thyroid nodules.