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Abstract #2129

Using Natural Language Processing to Explore the Correlation of Breast MR Findings and BI-RADS classification

Yuan Jiang1, Yi Liu1, Yahui Shi2, Zuofeng Li2, Juan Wei2, and Xiaoying Wang1

1Radiology, Peking University First Hospital, Beijing, People's Republic of China, 2Philips Research China, Shanghai, People's Republic of China

The decision tree trained on MR descriptions by natural language processing (NLP) method shows desirable capability in identifying the high-risk BI-RADS 5-6 class.From the decision path, we identify the key indicators to distinguish BI-RADS 5-6 from the relatively low-risk classes. And the inner heterogeneity of BI-RADS 4 cases makes it difficult to build a general model for this class.

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