Abstract #4878

MR-based machine learning radiomics can predict tumor heterogeneity and pathologic response after neoadjuvant therapy in HER2 breast cancer

Almir Bitencourt^1,2, Peter Gibbs³, Carolina Rossi Saccarelli³, Isaac Daimiel³, Roberto Lo Gullo³, Michael Fox³, Sunitha Thakur³, Katja Pinker³, Elizabeth A Morris³, Monica Morrow⁴, and Maxime Jochelson³

¹Breast Radiology, MSKCC, New York, NY, United States, ²Department of Imaging, A.C. Camargo Cancer Center, Sao Paulo, Brazil, ³MSKCC, New York, NY, United States, ⁴Breast Surgery, MSKCC, New York, NY, United States

In this study, we used magnetic resonance (MR)-based clinical and radiomic features to assess tumor heterogeneity in 311 HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC), and correlated these findings with tumor heterogeneity and pathologic response. Tumor heterogeneity was evaluated based on the HER2 expression (IHC vs. FISH) . Pathologic complete response (pCR) was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics analysis and machine learning with MRI were able to assess tumor heterogeneity and predict pCR after neoadjuvant chemotherapy in these patients, with a diagnostic accuracy of 97.4% and 85.2%, respectively.

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