To establish an ultrasonographic (US) prediction model for benign and malignant salivary gland tumors.
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.
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.
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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Published online: August 06, 2022
Accepted: July 27, 2022
Received in revised form: July 25, 2022
Received: March 4, 2022
LiuQing Peng and Nan Li are co-first authors.
© 2022 Elsevier Inc. All rights reserved.