A combined clinical-ultrasound radiomics model for differentiating benign and malignant BI-RADS category 4 breast masses.

Abstract

Purpose: To evaluate the diagnostic performance of a model combining gray-scale ultrasound (US) radiomic features and clinical data in distinguishing benign from malignant breast masses classified as Breast Imaging Reporting and Data System (BI-RADS) category 4.

Methods: In this retrospective study, 149 women with pathologically confirmed breast masses were included and randomly divided into a training cohort (n=104) and a validation cohort (n=45). A total of 1,046 radiomic features were extracted from US images. Feature selection was performed using Pearson correlation analysis followed by least absolute shrinkage and selection operator (LASSO) regression. Three K-nearest neighbor (KNN) classifiers were developed: a clinical model, an ultrasound radiomics (USR) model, and a combined clinical-USR model. Model performance was assessed using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).

Results: Seven radiomic features and two clinical variables were selected for model construction. In the training cohort, the combined clinical-USR model achieved an AUC of 0.927, with an accuracy of 89.0%, sensitivity of 88.9%, and specificity of 89.8%. In the validation cohort, the AUC of 0.826, with an accuracy of 80.0%, sensitivity of 83.3%, and specificity of 66.7%. The standalone USR model yielded AUCs of 0.902 and 0.883 in the training and validation cohorts, respectively, while the clinical model showed lower AUCs of 0.876 and 0.794. Decision curve analysis (DCA) indicated that the combined model provided a greater net clinical benefit than the clinical model alone.

Conclusion: The integration of ultrasound radiomic features with clinical data improves diagnostic performance in differentiating benign from malignant BI-RADS 4 breast masses. The combined model holds potential for aiding clinical decision-making but requires further validation in larger, independent datasets.