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Updated: Jun 6, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Nomogram for predicting malignancy in thyroid nodules using clinical, biochemical, ultrasonographic, and cytologic
Iain J Nixon1, Ian Ganly, Lucy E Hann
1Department of Head and Neck Surgery, Memorial Sloan Kettering Cancer Centre, New York, NY 10065, USA. nixoni@mskcc.org
Background:
Thyroid nodules often discovered incidentally and present a management problem particularly when investigations suggest atypical or suspicious cells. Prediction of the risk of malignancy within such a thyroid nodule is based on clinical, biochemical, ultrasonographic, and cytologic features. Our aim was to create a nomogram to predict accurately the chance of malignancy within a thyroid nodule.
Methods:
All patients with thyroid nodules who underwent ultrasonographic-guided fine needle aspiration and operative resection at our institution during 2007-2008 were identified. Clinical records, biochemical profiles, pathology reports, ultrasonographic images, and cytology slides were reviewed. A multivariate logistic regression was used to quantify the value of the variables in estimating the risk of malignancy.
Results:
The records of 158 patients with 190 nodules were reviewed. Eighteen nodules were excluded. The 8 variables with the greatest predictive value selected for the nomogram were biochemical (thyroid-stimulating hormone), ultrasonography (shape, echo texture, and vascularity), and cytology (nuclear grooves, pseudoinclusions, cellularity, and presence of colloid). The nomogram had an excellent predictive accuracy with a concordance index of 91%.
Conclusion:
We produced a nomogram that can quantify accurately the risk of malignancy in a thyroid nodule based on biochemical, ultrasonographic, and cytologic features.
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