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Updated: Jun 18, 2026
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
A clinically anchored radiomics dictionary for explainable TI-RADS-based thyroid nodule classification in ultrasound;
Mohammad Salmanpour1, Shahram Taeb2, Ali Fathi Jouzdani3
1Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; Technological Virtual Collaboration Company (TECVICO CORP.), Vancouver, BC, Canada.
European Journal of Radiology
|June 16, 2026
Summary
This study created an interpretable radiomics dictionary to link thyroid ultrasound features with TI-RADS categories, improving AI model trust for nodule classification. The framework enhances transparency in risk stratification using artificial intelligence.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Medicine
- Quantitative Ultrasound Imaging
Background:
- Artificial intelligence (AI)-based radiomics models for thyroid ultrasound (US) often lack interpretability, hindering clinical adoption and trust.
- Existing models struggle to connect quantitative US features to established semantic lexicons like the Thyroid-Imaging-Reporting-and-Data-System (TI-RADS).
Purpose of the Study:
- To develop and evaluate an interpretable radiomic feature (RF) framework for thyroid nodule classification.
- To link quantitative US features to the TI-RADS semantic lexicon using a clinically grounded radiomics dictionary.
- To enhance the transparency and clinical trust of AI-driven thyroid nodule risk stratification.
Main Methods:
- A radiomics dictionary was constructed, mapping TI-RADS categories to Image-Biomarker-Standardization-Initiative-compliant RFs from 2D US images.
- Expert consensus (physicians, physicists, radiology, biology experts) defined feature-to-lexicon relationships, analyzed with Shapley-Additive-Explanations (SHAP).
- A stability-aware composite scoring framework was used for robust model selection across 5,542 nodules from three multicenter datasets.
Main Results:
- The radiomics dictionary enabled direct interpretation of radiomic signatures within TI-RADS terminology.
- A Select-From-Model (logistic regression) plus Extra-Trees classifier achieved high testing performance (ROC-AUC: 0.941 ± 0.004).
- SHAP analysis identified texture heterogeneity (e.g., Gray Level Run Length Matrix non-uniformity) as a key malignancy indicator, aligning with high-risk TI-RADS descriptors.
Conclusions:
- An interpretable radiomics dictionary and a stability-aware model selection framework were successfully introduced.
- This approach addresses the interpretability limitations of AI radiomics models in thyroid ultrasound.
- The framework facilitates transparent and reliable thyroid nodule risk stratification using US data.
Keywords:
Explainable Artificial IntelligenceThyroid CancerThyroid Imaging Reporting and Data SystemThyroid Nodule ClassificationUltrasoundMore Related Videos
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