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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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Optimizing C-TIRADS for sub-centimeter thyroid nodules using machine learning-derived feature importance.

Dongming Guo1, Zhihui Lin1, Jiajia Wang1

  • 1Department of Interventional Ultrasound, Cancer Hospital of Shantou University Medical College, Shantou, China.

Frontiers in Endocrinology
|October 13, 2025
PubMed
Summary

Machine learning improved the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) for diagnosing small thyroid nodules. The enhanced system, using SHAP analysis, offers better accuracy and risk stratification for sub-centimeter nodules.

Keywords:
C-TIRADSSHAPmachine learningmicrocarcinomarisk stratificationsub-centimeter thyroid nodulesultrasound

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Sub-centimeter thyroid nodules pose diagnostic challenges.
  • Current Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) requires optimization for small nodules.

Purpose of the Study:

  • To enhance the diagnostic performance of C-TIRADS for sub-centimeter thyroid nodules.
  • To incorporate machine learning-derived feature importance into the C-TIRADS scoring system.

Main Methods:

  • Retrospective study with primary (741 patients) and validation (421 patients) cohorts.
  • SHapley Additive exPlanations (SHAP) quantified ultrasound feature importance using an XGBoost model.
  • Modified C-TIRADS scoring system developed with increased weight for the most predictive feature.

Main Results:

  • Vertical orientation identified as the most significant predictor of malignancy.
  • Modified C-TIRADS significantly improved diagnostic performance (AUC) in both cohorts (P < 0.001).
  • Substantial improvements in risk classification (NRI) and clinical decision-making (DCA) observed.

Conclusions:

  • The SHAP-guided modified C-TIRADS enhances diagnostic accuracy and risk stratification for sub-centimeter thyroid nodules.
  • This approach facilitates improved clinical decision-making for challenging thyroid nodules.