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Abstract #2077

Using ngram-based features to explore the correlation of prostate MR findings and PI-RADS classification

Shuai Ma1, Yi Liu1, Ge Gao1, Rui Wang1, Yahui Shi2, Zuofeng Li2, Juan Wei2, and Xiaoying Wang1

1Peking 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 represents a desirable performance in identifying low-risk PI-RADS 2-3 classes with high precision and high-risk PI-RADS 5 class with high recall. From the decision path, several specific features are adopted to make decision and the identification of key indicator contributes to distinguish PI-RADS 2 class from PI-RADS 3 class.

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