Microwave-Based Breast Density Assessment Using the SAFE Device.

Background

Breast density is a critical factor in both breast cancer risk and the effectiveness of diagnostic technologies. Women with dense breast tissue not only have a higher risk of developing breast cancer but also face challenges in screening and diagnosis using mammography. Microwave-based breast evaluation is an emerging approach that can offer complementary information about breast density without relying on ionizing radiation. Incorporating breast density assessment into microwave-based evaluation may represent a step toward exploring the clinical utility of SAFE, given the established association between breast density and breast cancer risk.

Methods

We investigated whether data acquired from the SAFE device could be used to classify breast density levels using machine-learning algorithms. The real and imaginary components of the S11 and S21 parameters were used as input features. Multiple machine-learning models were evaluated, and classification performance was assessed using standard metrics.

Results

Among the three machine-learning algorithms evaluated, the best classification performance was achieved using the Support Vector Machine (SVM) model. Results showed promising classification performance with 84% average accuracy, 80% average sensitivity, and 86% average specificity.

Conclusion

Our preliminary findings demonstrate that SAFE can be used to classify breast density, potentially complementing existing diagnostic methods. These findings support further investigation of microwave-based breast density assessment as a potentially useful source of clinically relevant breast density information.