Meeting Banner
Abstract #4877

Characterization of Sub-centimeter Enhancing Breast Masses on MRI with Radiomics and Machine Learning in BRCA Mutation Carriers

Roberto Lo Gullo1, Isaac Daimiel 1, Carolina Saccarelli1, Almir Bitencourt1, Peter Gibbs2, michael James Fox2, Sunitha B. Thakur2,3, Danny F. Martinez1, Elisabeth A. Mossir1, and Katja Pinker1
1Radiology, Breast imaging, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 3medical physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States

The purpose of our study was to investigate whether radiomics features extracted from MRI of BRCA-positive patients with breast masses smaller than 1 cm coupled with machine learning can differentiate benign from malignant lesions using model-free parameter maps. We included 96 patients with 116 lesions assessed by two readers according to the BI-RADS lexicon. Radiomics features were calculated and included in a machine learning model, along with clinical factors, to discriminate between malignant and benign lesions. The machine learning model, combining two clinical and three radiomics features, achieved higher diagnostic accuracy (81.5%) compared to morphological assessment alone (53.4%)

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here