Automatic extraction of BI-RADS breast tissue composition classes from mammography reports Bethany Percha (Stanford) Houssam Nassif (U. Wisconsin) Jafi.

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Automatic extraction of BI-RADS breast tissue composition classes from mammography reports Bethany Percha (Stanford) Houssam Nassif (U. Wisconsin) Jafi Lipson (Stanford) Elizabeth Burnside (U. Wisconsin) Daniel Rubin (Stanford)

Breast density is an important aspect of the radiological evaluation of the breast. Dense fibroglandular tissue is a risk factor for breast cancer. Dense tissue decreases mammographic sensitivity. Breast tissue composition is partially genetic.

The BI-RADS system divides breast composition into four categories. 1. Fatty 2. Scattered fibroglandular 3. Heterogeneously dense 4. Dense

These standardized categories... Help stratify patients at time of screening. Enable radiologists to quality observations with discussion of how mammographic sensitivity may limit them. Minimize ambiguity.

Limitations of the BI-RADS system Breast composition information typically reported as part of narrative text. No one textual pattern can extract composition information with 100% accuracy. Large research studies require information from thousands of reports.

Our approach... Borrow techniques from text-mining to extract breast tissue composition information quickly and easily. Use pattern-matching and regular expression to identify and extract descriptions.

Methods: Data Three distinct mammography corpora from Stanford, UCSF, and the Marshfield Clinic. Used 34,489 reports from Stanford’s radTF database and 146,972 reports from UCSF Medical Center to construct set of textual patterns indicative of each breast composition class. Independent test set composed of 500 annotated reports from Stanford and 100 from the Marshfield Clinic.

Methods: Evaluation Reports independently annotated by radiologists. Annotators blinded to automatically- extracted composition classes when assessing reports.

Methods: Rule Construction Using free-text mammography reports as its input, our algorithm classifies each into one of five classes: predominantly fat (class 1) scattered densities (class 2) heterogeneously dense (class 3) dense (class 4) no descriptors present (class 5) Annotators blinded to automatically- extracted composition classes when assessing reports.

Results