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Abstract #2316

Artificial Intelligence Prediction of Breast Cancer Pathologic Complete Response from Axillary Lymph Node MRIs

Janice Yang1,2, Thomas Ren1, Hongyi Duanmu3, Pauline Huang1, Renee Cattell1, Haifang Li1, Fusheng Wang3, and Tim Q Duong1
1Radiology, Stony Brook University, Stony Brook, NY, United States, 2Dougherty Valley High School, San Ramon, CA, United States, 3Computer Science, Stony Brook University, Stony Brook, NY, United States

Breast cancer patient response to neoadjuvant chemotherapy cannot be accurately predicted or monitored through imaging, leading to unnecessary treatment and sentinel lymph node biopsies. We developed convolutional neural networks to predict pathologic complete response utilizing a combination of axillary lymph node MRIs from before and during treatment. 3-fold cross validation reveals that the model trained on scans before and after the first cycle of neoadjuvant chemotherapy performed best with an accuracy of 81.17%. These results point to improved predictive performance of early imaging markers in axillary lymph nodes and encourages its implementation to aid treatment planning and improve prognosis.

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