Abstract #1212

Combining radiomic features with background parenchymal enhancement from DCE-MRI data for predicting treatment response in breast cancer

Kalina Polet Slavkova¹, Eric A Cohen¹, Rhea Chitalia², Snekha Thakran¹, Walter C Mankowski¹, Alex Nguyen², Hannah Horng², Elizabeth S McDonald³, Michael D Feldman⁴, Angela DeMichele^5,6, and Despina Kontos⁷

¹Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States, ⁴Division of Surgical Pathology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States, ⁵Division of Hematology Oncology, Perelman Center for Advanced Medicine, Philadelphia, PA, United States, ⁶Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States, ⁷Radiology, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

Keywords: Breast, Breast, radiomics, DCE-MRIOur objective is to predict pathological complete response (pCR) outcome to neoadjuvent chemotherapy in breast cancer patients. We combine radiomic features with background parenchymal enhancement -- computed from standard-of DCE-MRI data from the ISPY-2 trial -- and model treatment outcome via multivariable logistic regression. During training and testing, we demonstrate that models including BPE alongside radiomic and clinical covariates yielded the highest AUC values among all tested regression models, thus improving the prediction of pCR outcome.

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