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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