1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

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

8. 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