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Abstract #3464

Radiomics to Predict Pathological Complete Response in Patients with Triple Negative Breast Cancer

Michael Hirano1, Anum S. Kazerouni2, Mladen Zecevic2, Laura C. Kennedy3, Shaveta Vinayak2, Habib Rahbar2, Matthew J. Nyflot2, Suzanne Dintzis2, and Savannah C. Partridge2
1University of Washingon, Seattle, WA, United States, 2University of Washington, Seattle, WA, United States, 3Vanderbilt University, Nashville, TN, United States


Radiomics is an advancing field of medical image analysis based on extracting large sets of quantitative features that can be used for outcome modeling for clinical decision support. Our study investigated the value of radiomics features extracted from pre-treatment dynamic contrast-enhanced MRI for the prediction of neoadjuvant chemotherapy response in patients with triple-negative breast cancer. In a retrospective cohort of 103 TNBC patients, radiomics-based models using post-contrast images and kinetics maps were moderately predictive of pathologic response, and lesion size and shape features were the most consistent predictors across all image types.

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