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Abstract #0572

Early Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using Multi-Modal Diffusion MRI with Machine-Learning

Muge Karaman1,2, Shunan Che3, Rahul Mehta1,2, Guangyu Dan1,2, Zheng Zhong1,2, Han Ouyang3, X. Joe Zhou1,4, and Xinming Zhao3
1Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States, 2Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, 3Department of Radiology, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China, 4Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

An early imaging assessment of breast cancer’s response to neoadjuvant chemotherapy (NAC) is critical for timely planning of treatment strategies. In this study, we develop a machine-learning-based approach to investigate whether the combined features obtained from the intravoxel incoherent motion and continuous-time random-walk diffusion models provide an early prediction of pathologic response in patients receiving NAC. Our results have shown that a gradient boosting classifier trained with the early-treatment parametric changes within tumor can predict the response with an accuracy that is 96% of the accuracy achieved by using the post-treatment parametric changes.

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