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Updated: Jan 10, 2026
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Early prediction of thyroid capsule invasion in papillary microcarcinoma using ultrasound-based deep learning models:
Lin Sui1,2,3,4, Bojian Feng1,2,3,4, Xiayi Chen1,5
1Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Objective:
Thyroid capsule invasion (TCI) predicts early progression in papillary thyroid microcarcinoma (PTMC). This study aimed to develop an integrated model that combines handcrafted peri-tumoral radiomics features with deep learning (DL)-derived intra-tumoral features for accurate early prediction of TCI, to support clinical decision-making.
Materials And Methods:
Retrospective data from 964 patients with 964 pathologically confirmed PTMC lesions across three centers were collected. Radiomics features were extracted from multiple peri-tumoral regions, and the optimal peri-tumor region with the best radiomics features was selected using a support vector machine (SVM). The selected radiomics features were then combined with intra-tumoral DL features extracted from the tumors before being fed into four different DL models for training and validation. Performance was validated on the internal (n = 177) and external (n = 84) test sets. Six radiologists (senior/attending/junior) assessed TCI with/without DL assistance.
Results:
The radiomics features, which achieved the best diagnostic performance with an AUC of 0.795 using SVM, were extracted from the peri-tumor region with 30% expansion from the original tumor. By further combining these radiomics features with intra-tumoral DL features, four different DL models were established to identify TCI in PTMC. Swin-Transformer achieved superior performance (internal AUC: 0.923; external AUC: 0.892). With DL model assistance, the AUCs of six radiologists significantly improved, for example, from 0.720 to 0.796 and from 0.725 to 0.790 for senior radiologists, and similar gains were observed for attending and junior radiologists.
Conclusions:
As an effective clinical assistive tool, this integrated model can provide TCI identification with high level of accuracy. With its ability to enhance radiologists' diagnostic performance, it supports early PTMC risk stratification and personalized intervention.
Critical Relevance Statement:
This retrospective multicenter study establishes an integrated model for identifying TCI in PTMC. The model significantly enhances radiologists' diagnostic precision across multiple experience levels, supporting early clinical decision-making for optimized intervention strategies.
Key Points:
Accurate prediction of TCI facilitates early assessment of PTMC progression and guides subsequent individualized clinical management. DL significantly improves the predictive performance for TCI. DL effectively assists radiologists in TCI diagnosis.
