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Abstract #4876

Can radiomics and machine learning capture the unique differences between invasive lobular and invasive ductal carcinoma of the breast?

Carolina Rossi Saccarelli1,2, Peter Gibbs3, Almir G V Bitencourt1, Isaac Daimiel1, Roberto Lo Gullo1, Sunitha B Thakur3, Elizabeth A Morris1, and Katja Pinker 1
1breast radiology, MSKCC, New York, NY, United States, 2breast radiology, Hospital Sirio-Libanes, Sao Paulo, Brazil, 3MSKCC, New York, NY, United States

In this study, we hypothesized that the specific genomic profiles of invasive lobular carcinoma (ILC) can be captured with radiomics analysis and machine learning (ML) from standardized dynamic contrast-enhanced breast MRI. Three-dimensional tumor segmentation of the first post-contrast T1-weighted sequence was conducted and included the entire mass and non-mass enhancement lesions, unifocal and multifocal/multicentric lesions. This supervised ML model produced an accuracy of 76.6%, sensitivity of 72.7%, specificity of 80.6%, PPV of 79.1% and NPV of 74.5%. Our preliminary results indicate that radiomics analysis coupled with supervised ML allows a non-invasive differentiation between ILC and invasive ductal carcinoma.

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