Natural Language Processing based Obtaining Information in Pathology Report of Breast Cancer: Single-Institution Study

Background: In 2018, breast cancer was the second most common cancer worldwide. Pathology reports provide important information for optimal treatment decision making. Objective: To elucidate the deployment of deep learning data extraction methods for pathology reports in a single institute, we investigated the performance of methods between regular expression and natural language processing (NLP) in terms of accuracy. Methods: : This was compared to the bidirectional encoder representations from transformers (BERT) model using specific vocabulary such as BERT-basic, BioBERT, and ClinicalBERT. A total of 1,215 pathology reports were used to build annotated data to develop an extraction algorithm for pathology reports. K-fold cross-validation was used to verify the performance of BioBERT and ClinicalBERT, pre-trained in the BERT model. Results: : Among them, BioBERT emerged as a highly accurate (0.99901) data parsing model based on by k-fold validation. The parsing method using the NLP model could obtain data with higher overall accuracy than the existing method using regular expressions. Conclusions: : Our results showed that BioBERT has high accuracy in pathology reports, and that the NLP model can obtain data with a higher overall accuracy than regular expressions. Taken together, our findings suggest that the process of obtaining information from pathology reports should include NLP using BioBERT.