Automatic Extraction of BI-RADS Features from Cross-Institution and Cross-Language Free-Text Mammography Reports Houssam Nassif, Terrie Kitchner, Filipe Cunha, Inês C. Moreira, and Elizabeth S. Burnside

Mammogram Radiologist Structured Database Impression (free text) Predictive Model Benign Malignant Breast cancer screening starts with a mammogram, which is read by a radiologist. The radiologist takes some measurements, checks for features, and records them in a database. Various successful predictive models have been built on top of these databases But the amount of tabulated information varies between institutions, since radiologists also record their impression as free-text. These free-text reports are not used in any predictive model, leading to loss of information. In this work, we extract information from free-text reports, in order to augment breast-cancer classification models. 2

BI-RADS Lexicon Concepts Radiologists use a particular lexicon to describe their findings It is the BI-RADS lexicon, which stands for Breast Imaging Reporting and Data System It depicts 43 distinct mammography concepts Our task is basically to map text to these concepts Concepts 3

Example In the right breast, an approximately 1.0 cm mass is identified in the right upper inner breast. This mass is noncalcified and partially obscured and lobulated in appearance. Lobular Shape Oval Shape Obscured Margin … Report 1 1 Report 2 As an example, the following free-text report contains both the “lobular shape” and the “obscured margin” concepts. We identify their underlying text and populate a database 4

Our approach consists of three modules, (a syntax analyzer, a semantic parser and a lexical scanner) and incorporates the BI-RADS lexicon and experts knowledge. Our first step is a preprocessing step 5

Materials & Methods Training on 146,972 mammograms Testing on same academic institution as training and on different private institution Replicate algorithm into Portuguese language, and test on Portuguese set All test sets manually annotated

Results Testing set Size Precision Recall Academic 100 99.1% 98.2% Private 71 97.9% 95.9% Portuguese 306 96.6% 92.6%

Conclusion Automated BI-RADS extractor: Performs no worse than manual extraction Generalizes across institutions Generalizes across languages Enables the incorporation of free-text data into breast cancer risk prediction tools