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Abstract #1445

Radiomics and Machine Learning on multiparametric MRI for breast cancer diagnosis

Isaac Daimiel Naranjo1, Roberto Lo Gullo2, Carolina Sacarelli2, Almir Bitencourt2, Peter Gibbs2, Elisabeth Morris2, Caleb Sooknanan2, Jeff Reiner2, Maxine S Jochelson2, Sunitha Thakur2, and Katja Pinker-Domenig2
1Radiology, Memorial Sloan Kettering Cancer Center, NEW YORK, NY, United States, 2Memorial Sloan Kettering Cancer Center, New york, NY, United States

Radiomics coupled with machine learning is based on the extraction of signatures from medical images that are invisible to the human eye to create models which would improve breast cancer diagnosis. Radiomics features extracted from dynamic contrast-enhanced MRI and diffusion-weighted imaging can be combined in multiparametric MRI. We hypothesize that radiomics features extracted from multiparametric MRI would allow for an improved model affording a more accurate breast cancer diagnosis. We developed a multiparametric model that achieved the best accuracy for breast cancer diagnosis compared to models based on dynamic contrast-enhanced MRI or diffusion-weighted imaging.

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