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Updated: Dec 21, 2025
Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
Partially cystic thyroid cancer on conventional and elastographic ultrasound: a retrospective study and a machine
Hai-Na Zhao1, Jing-Yan Liu1, Qi-Zhong Lin2
1Department of Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, China.
Background:
Thyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning-assisted system based on ultrasound (US) and elastography.
Methods:
Patients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated.
Results:
A total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model.
Conclusions:
US features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.
