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This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included in the study, with 12 malignant lesions and 33 benign lesions. The study showed how feasible a radiomic approach was in the characterization of enhancing foci on breast MRI.

Key points

  • Radiomic signature could distinguish malignant from benign enhancing foci on magnetic resonance imaging of the breast.
  • In this study, we applied a “training with input selection and testing “machine learning algorithm on 45 foci, using 8 confirmed benign lesions and 15 confirmed malignant lesions as reference cases.
  • Over 200 radiomic features were extracted.
  • Overall, a k-nearest neighbour classifier based on 35 selected features showed an over 90% accuracy.

Article: A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

Authors: Natascha C. D’Amico, Enzo Grossi, Giovanni Valbusa, Francesca Rigiroli, Bernardo Colombo, Massimo Buscema, Deborah Fazzini, Marco Ali, Ala Malasevschi, Gianpaolo Cornalba & Sergio Papa

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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.