Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a...

To differentiate benign and malignant breast masses by extracting radiomic features from low-energy and recombined contrast-enhanced mammography (CEM) images and to evaluate the diagnostic performance of multiple machine learning classifiers.

In this retrospective, single-center study, 145 patients who underwent CEM between February 2019 and January 2022 were included. Radiomic features were extracted from manually segmented regions of interest on low-energy and recombined images using an open-source workflow (ITK-SNAP and PyRadiomics). The dataset was split at the patient level into a training set (75%) and an independent test set (25%); within the training set, feature selection and model optimization were performed using 10-fold cross-validation. Diagnostic performance [as measured by area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value] was reported on the held-out independent test set.

Ensemble learning demonstrated the best performance for both image types. The highest accuracy and AUC were 91.8% and 0.978 for recombined images and 89.7% and 0.968 for low-energy images, respectively. For recombined images, ensemble learning yielded the highest sensitivity (91.8%), whereas neural networks achieved the highest specificity (95.8%). For low-energy images, ensemble learning reached the highest sensitivity (98.0%), and decision trees achieved the highest specificity (91.7%).

Radiomics analysis of CEM images can effectively differentiate between benign and malignant breast masses, potentially enhancing diagnostic accuracy in breast imaging.

A radiomics workflow based on recombined CEM images and open-source tools may complement conventional CEM interpretation, improve non-invasive lesion characterization, and support further research toward clinically validated decision-support applications.