Objective
To establish an ultrasonographic (US) prediction model for benign and malignant salivary
gland tumors.
Study Design
We retrospectively analyzed the clinical data of 575 patients with salivary gland
tumors. Patients were divided into benign (N = 420) and malignant (N = 155) tumor groups based on histopathologic results. The clinical and US features
of the tumor groups were statistically compared. With histopathologic findings as
the dependent variable and clinical and US features as independent variables, a multiple
logistic regression model was established. The area under the receiver operating characteristic
curve (AUC) was calculated to evaluate its diagnostic efficacy.
Results
Statistically significant differences between tumor groups were discovered for patient
age, tumor site, and the US features of tumor size, shape, and margins; posterior
echo pattern; microcalcification, abnormal lymph nodes, and tumor vascularity. Individual
US features had limited diagnostic value. The AUC, sensitivity, specificity, and accuracy
values of the logistic regression equation were 0.893, 84.3%, 80.0%, and 83.1%, respectively.
Conclusion
The diagnostic performance of the predictive model was significantly better than that
of any single US factor. This suggests that establishment of multiple models based
on US features can improve the accuracy of diagnosis of benign and malignant salivary
gland tumors and can be applied clinically.
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Article info
Publication history
Published online: August 06, 2022
Accepted:
July 27,
2022
Received in revised form:
July 25,
2022
Received:
March 4,
2022
Footnotes
LiuQing Peng and Nan Li are co-first authors.
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
