Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning in order to differentiate benign lesions from malignant lesions using model-free parameter maps. The authors determined that radiomics analysis coupled with machine learning does improve the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers.

Key points

• Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign.
• Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

Article: Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

Authors: Roberto Lo Gullo, Isaac Daimiel, Carolina Rossi Saccarelli, Almir Bitencourt, Peter Gibbs, Michael J. Fox, Sunitha B. Thakur, Danny F. Martinez, Maxine S. Jochelson, Elizabeth A. Morris & Katja Pinker