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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