Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System

Author:

Fu Chia-Po1234,Yu Ming-Jen1,Huang Yao-Sian5,Fuh Chiou-Shann16,Chang Ruey-Feng16

Affiliation:

1. Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan

2. Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan

3. Department of Medicine, Chung Shan Medical University, Taichung, Taiwan

4. Department of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

5. Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua County, Taiwan

6. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

Abstract

Abstract Introduction The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules. Materials and Methods Ultrasound images were collected prospectively from patients who received fine needle aspiration biopsy for thyroid nodules from January 2016 to June 2021. One hundred thirty-nine patients with malignant thyroid nodules were enrolled, while 235 patients with benign nodules served as controls. Images were fed to Swin-T and ResNeSt50 models to classify the thyroid nodules. Results Patients with malignant nodules were younger and more likely male compared to those with benign nodules. The average sensitivity and specificity of Swin-T were 82.46% and 84.29%, respectively. The average sensitivity and specificity of ResNeSt50 were 72.51% and 77.14%, respectively. Receiver operating characteristics analysis revealed that the area under the curve of Swin-T was higher (AUC=0.91) than that of ResNeSt50 (AUC=0.82). The McNemar test evaluating the performance of these models showed that Swin-T had significantly better performance than ResNeSt50.Swin-T classifier can be a useful tool in helping shared decision-making between physicians and patients with thyroid nodules, particularly in those with high-risk characteristics of sonographic patterns.

Publisher

Georg Thieme Verlag KG

Subject

Endocrinology,General Medicine,Endocrinology, Diabetes and Metabolism,Internal Medicine

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