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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

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.