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Updated: Jun 6, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS
Pilar López-Úbeda1, Teodoro Martín-Noguerol2, Alba Ruiz-Vinuesa3
1NLP Department, HT Médica, Carmelo Torres 2, 23007, Jaén, Spain. p.lopez@htmedica.com.
Objective:
The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore how Natural Language Processing (NLP) leveraging Transformers models can classify ACR TI-RADS from text reports using the description of thyroid nodule features.
Materials And Methods:
This retrospective study evaluated 16,847 thyroid-free text reports from our institution. An automated system, followed by manual review by a radiologist, established baseline annotations by assigning ACR TI-RADS categories from 1 to 5. Two types of systems were evaluated and compared in the dataset. The first by performing a multiclass classification to detect the associated ACR TI-RADS, and the second by extracting thyroid nodule features from the textual reports and incorporating them into the classifier.
Results:
Our study showed that models enhanced with specific features systematically outperformed those without. Particularly, the BERTIN model, to which additional features were added, achieved the highest level of accuracy, with a score of 0.8426. Moreover, we found a correlation between the presence of punctate echogenic foci, a feature often linked to malignant thyroid lesions, and increased ACR TI-RADS scores.
Conclusions:
The features of the thyroid nodules described in thyroid US reports, such as composition, echogenicity, shape, margin or echogenic foci, help the NLP classifier to predict the associated ACR TI-RADS most accurately.
