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Related Experiment Video

Updated: May 12, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.

Fuqiang Ma1,2, Fengchang Yu3, Shenhui Lv4

  • 1Department of Integrated Traditional and Western Medicine,The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.

BMJ Open
|May 7, 2025
PubMed
Summary

This study developed a machine learning model to distinguish malignant from benign thyroid nodules using routine data. The Gradient Boosting model, utilizing thyroid function tests and antibody levels, showed promising diagnostic accuracy.

Keywords:
DIABETES & ENDOCRINOLOGYMachine LearningThyroid disease

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Area of Science:

  • Endocrinology
  • Oncology
  • Medical Informatics

Background:

  • Thyroid nodules (TNs) are common, with a significant proportion being malignant, necessitating accurate diagnostic tools.
  • Current diagnostic methods for TNs can be invasive or lack definitive accuracy, leading to unnecessary procedures.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for differentiating malignant from benign TNs using routine clinical data.
  • To provide diagnostic assistance to medical professionals for improved TN evaluation.

Main Methods:

  • A cohort of 1649 patients with TNs was analyzed, stratifying by gender, age, and thyroid function parameters (free triiodothyronine [FT3], free thyroxine [FT4], thyroid peroxidase antibody [TPOAB]).
  • Seven ML models were developed and evaluated, including Random Forest, Decision Tree, Logistic Regression, K-Neighbours, Gaussian Naive Bayes, Multilayer Perception, and Gradient Boosting.
  • Performance was assessed using metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 score.

Main Results:

  • The Gradient Boosting model demonstrated the best performance, achieving an AUC of 0.82, accuracy of 79.4%, and precision of 0.814.
  • Multivariate analysis revealed significant differences between TNs and thyroid cancer groups in gender, age, FT3, FT4, and TPOAB.
  • FT4, TPOAB, and FT3 were identified as the top three predictive features in the Gradient Boosting model.

Conclusions:

  • A novel predictive model for benign and malignant TNs was developed using the Gradient Boosting Decision Tree algorithm.
  • The study validates the clinical predictive value of thyroid function parameters (FT4, FT3) and TPOAB as key biomarkers for TN assessment.
  • The developed ML model offers a non-invasive tool to aid in the differential diagnosis of thyroid nodules.