Abstract #0278

Combination of MRI quantitative measures improves prediction of residual disease following neoadjuvant chemotherapy (NAC) for breast cancer in the I-SPY 2 TRIAL

Wen Li¹, David C. Newitt¹, Lisa J. Wilmes¹, Ella F. Jones¹, Jessica Gibbs¹, Elizabeth Li¹, Bo La Yun¹, John Kornak², Bonnie N. Joe¹, Christina Yau³, On behalf of the I-SPY 2 Consortium⁴, Laura J. Esserman³, and Nola M. Hylton¹

¹Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States, ³Surgery, University of California San Francisco, San Francisco, CA, United States, ⁴Quantum Leap Healthcare Collaborative, San Francisco, CA, United States

This abstract presents the work of combining different MR measures to predict primary tumor residual after patients with breast cancer went through neoadjuvant chemotherapy. Three types of MR measures are investigated in this study: longest diameter, functional tumor volume, and apparent diffusion coefficient. Results showed that when all three types of MR measures are combined in the logistic regression model, it yielded the highest AUC compared to the model with only one of the MR measures. Results also suggested that measures taken at various treatment time points, not just pre-surgery, should be included in the prediction of the residual disease.

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