Extracting BI-RADS Features from Portuguese Clinical Texts

1. Extracting BI-RADS Features from Portuguese Clinical Texts H. Nassif, F. Cunha, I.C. Moreira, R. Cruz-Correia, E. Sousa, D. Page, E. Burnside, and I. Dutra University of Wisconsin – Madison, and University of Porto, Portugal

2. The American Cancer Society, Cancer Facts & Figures 2009.

3. Mammogram Radiologist Structured Database Impression (free text) Predictive Model Benign Malignant

4. BI-RADS Lexicon Concepts

5. Example • In the right breast, an approximately 1.0 cm mass is identified in the right upper slightly inner breast. This mass is noncalcified and partially obscured and lobulated in appearance. Concepts

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7. Syntax Analyzer • Tokenize sentences • Discard punctuation • Keep stop words • Stem words

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9. Information from Lexicon • Translate lexicon into Portuguese • Lexicon specifies synonyms: Eg: Equal density, Isodense • Lexicon allows for ambiguous wording:

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11. Experts • Provide domain specific information • Synonyms: Oval, Ovoid • Acronyms, abbreviations • Domain idiosyncrasies • Interact with and modify semantic rules

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13. Concept Finder • Regular expression rules • Extract concepts from text • Rule formation: • Initial rules based on lexicon • Rules refined by experts

14. Rule Generation Example 1 • Aim: Regional Distribution Concept • Lexicon specifies the word “regional” • Initial rule: presence of the word “regional” • Run on training set, experts see results • Many false positives: • “regional medical center”, “regional hospital” • Rule refined by experts: • “regional .* !(medical|hospital)”

15. Rule Generation Example 2 • Aim: Skin Thickening Concept • Lexicon specifies “skin thickening” • Try “skin” and “thickening” in same sentence • “skin retraction and thickening” • “thickening of the overlying skin” • “A BB placed on the skin overlying a palpable focal area of thickening in the upper outer right breast” • Experts suggest “skin” and “thickening” in close proximity

16. Scope • Scope: distance between two words • Start with a large scope: • assess number of true and false positives • Move to smaller scopes: • assess number of false negatives • Check precision and recall estimates • Experts decide on the best distance

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18. Negation Detector • Negation triggers (Mutalik 01, Gindl 08): • “não” (not) when not preceded by “onde” (where) • “sem” (without) • “nem” (nor). • Precedes or appears within the subsentence • Establish negation scope • “without evidence of suspicious cluster of microcalcifications”

19. Dataset • Training set: 1,129 reports, unlabeled • Testing set: 153 pairs, labeled by radiologist • Basic screening report • Detailed diagnostic report • Perform three refinement passes • Double blind, based on lexicon • Refine rules • Refine manual labeling and rules

20. Results

21. Conclusion • Out of 48 disputed cases, parser correctly classified 25 (52.1%) • First Portuguese BI-RADS extractor • Discovers features missed or misclassified • Similar performance to manual annotation • Method portable to other languages