Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System
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. [...] Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Ge...
Source: Experimental and Clinical Endocrinology and Diabetes - Category: Endocrinology Authors: Fu, Chia-Po Yu, Ming-Jen Huang, Yao-Sian Fuh, Chiou-Shann Chang, Ruey-Feng Tags: Article Source Type: research
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