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Related Experiment Video

Updated: Jun 4, 2026

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

A multicenter, clinically interpretable prediction model for malignancy risk in C-TIRADS 3-4 thyroid nodules.

Wei Liu1,2, Quan Xie3,4, Chongmei Liu1

  • 1Department of Pathology, Yueyang People's Hospital of Hunan Normal University, Yueyang, China.

Frontiers in Oncology
|June 3, 2026
PubMed
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A logistic regression model integrating ultrasound and lab data offers a reliable tool for assessing thyroid nodule malignancy risk. This clinically interpretable model aids decision-making for C-TIRADS 3-4 nodules.

Area of Science:

  • Endocrinology
  • Medical Imaging
  • Oncology

Background:

  • Thyroid nodules are common, and accurate malignancy risk assessment is crucial for patient management.
  • Current assessment often relies on ultrasonographic features and clinical factors, but improved prediction models are needed.

Purpose of the Study:

  • To develop and validate prediction models integrating ultrasonographic features and laboratory indicators for thyroid nodule malignancy risk.
  • To evaluate the clinical utility of these models in supporting decision-making for C-TIRADS 3-4 nodules.

Main Methods:

  • A multicenter retrospective study involving 631 nodules (modeling cohort) and 193 nodules (external validation cohort).
  • Logistic regression, random forest, SVM, XGBoost, and LightGBM models were built using ultrasound and laboratory data.
Keywords:
Clinical decision supportmachine learningnomogramrisk stratificationthyroid nodulesultrasound

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  • Model performance was evaluated using AUC in internal and external validation sets.

    • Main Results:

    • Logistic regression achieved an AUC of 0.924 internally and 0.929 externally, demonstrating stable performance.
    • In external validation, logistic regression showed significantly higher AUCs than other machine learning models after correction.
    • The logistic regression model exhibited good calibration and clinical utility, leading to a nomogram for risk assessment.

    Conclusions:

    • A logistic regression model using routine clinical and ultrasound data provides a stable, interpretable tool for assessing malignancy risk in C-TIRADS 3-4 thyroid nodules.
    • The developed nomogram aids in individualized risk assessment and supports clinical decision-making.