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Updated: Apr 3, 2026
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Ultrasound-based clinical prediction rule model for detecting papillary thyroid cancer in cervical lymph nodes: A
Nayana U Patel1, Kristin McKinney1, Sarah M Kreidler1
1Department of Radiology, University of Colorado School of Medicine, Aurora, CO, 80045.
Purpose:
To identify sonographic features of cervical lymph nodes (LNs) that are associated with papillary thyroid cancer (PTC) and to develop a prediction model for classifying nodes as metastatic or benign.
Methods:
This retrospective study included the records of postthyroidectomy patients with PTC who had undergone cervical ultrasound and LN biopsy. LN location, size, shape, hilum, echopattern, Doppler flow, and microcalcifications were assessed. Model selection was used to identify features associated with malignant LNs and to build a predictive, binary-outcome, generalized linear mixed model. A cross-validated receiver operating characteristic analysis was conducted to assess the accuracy of the model for classifying metastatic nodes.
Results:
We analyzed records from 71 LNs (23 metastatic) in 44 patients (16 with PTC). The predictive model included a nonhomogeneous echopattern (odds ratio [OR], 5.73; 95% confidence interval [CI], 1.07-30.74; p = 0.04), microcalcifications (OR, 4.91; 95% CI, 0.91-26.54; p = 0.06), and volume (OR, 2.57; 95% CI, 0.66-9.99; p = 0.16) as predictors. The model had an area under the curve of 0.74 (95% CI, 0.60-0.85), sensitivity of 65% (95% CI, 50% to 78%), and specificity of 85% (95% CI, 73% to 94%) at the Youden optimal cut point of 0.38.
Conclusions:
Nonhomogeneous echopattern, microcalcifications, and node volume were predictive of malignant LNs in patients with PTC. A larger sample is needed to validate this model.
