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Abstract #0601

Convolutional neural network classification of axillary lymph node metastasis on MRI of breast cancer patients

Thomas Ren1, Hongyi Duanmu1, Renee Cattell1, Rami Vanguri2, Mousumi Roy1, Michael Z Liu2, Vincent Zhang1, Sachin Jambawalikar2, Fusheng Wang1, and Tim Duong1

1Stony Brook University, Stony Brook, NY, United States, 2Columbia University, New York City, NY, United States

The majority of breast cancer metastasis spreads through the axillary lymph nodes. It is challenging to classify whether there is disease or no-disease axillary lymph nodes because they are small and cluster together. We implemented a convolutional-neural network for automatic classification of diseased versus non-diseased axillary lymph nodes by analyzing data from standard clinical breast MRI. Data were assigned randomly to 70/30 as training/validation set. The results showed the remarkable agreement with ground truths, with 86.7% accuracy. This approach may prove useful for automatically detecting lymph nodes metastasis on MRI in clinical settings in breast cancer patients.

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