Abstract #0184

Multi-modal MRI Parametric Maps Combined with Receptor Information to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

Hakmook Kang^1,2, Allison Hainline¹, Xia Li³, Lori R. Arlinghaus⁴, Vandana G. Abramson^5,6, A. Bapsi Chakravarthy^5,7, Brian Bingham⁸, and Thomas E. Yankeelov^2,4,5,9

¹Biostatistics, Vanderbilt University, Nashville, TN, United States, ²Center for Quantitative Science, Vanderbilt University, Nashville, TN, United States, ³GE Global Research, Niskayuna, NY, United States, ⁴Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ⁵Ingram Cancer Center, Vanderbilt University, Nashville, TN, United States, ⁶Medical Oncology, Vanderbilt University, Nashville, TN, United States, ⁷Radiation Oncology, Vanderbilt University, Nashville, TN, United States, ⁸School of Medicine, Vanderbilt University, Nashville, TN, United States, ⁹Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States

Pathologic complete response (pCR) following neoadjuvant chemotherapy is used as a short term surrogate marker of ultimate outcome in patients with breast cancer. Current imaging tools are suboptimal in predicting this response. Analyzing voxel-level heterogeneity in multi-modal MRI maps in conjunction with receptor status data, i.e., DCE- and DW-MRI, and ER/PR/HER2 status, allows us to improve the predictive power after the first cycle of neoadjuvant chemotherapy (NAC).

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