Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer

OBJECTIVES: This study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer.

METHODS: Clinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma’anshan People’s Hospital from January 2019 to December 2020 were retrospectively analyzed.

The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules).

The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed.

We used a chi-square test for categorical variables and a t-test for continuous variables.

Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression.

Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software.

RESULTS: Multivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young’s modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05).

Based on the shape (OR = 5.160, 95% CI: 2.252–11.825), the margin (OR = 9.647, 95% CI: 2.048–45.443), the echogenicity (OR = 6.512, 95% CI: 1.729–24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118–3.756), and the maximum Young’s modulus (SWE-max) (OR = 1.296, 95% CI: 1.140–1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403–2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model.

The bootstrap self-sampling method was used to verify the model.

The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer.

CONCLUSIONS: The nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.