1. Uncovering Age-Specific Invasive and DCIS Breast Cancer Rules Using Inductive Logic Programming Houssam Nassif, David Page, Mehmet Ayvaci, Jude Shavlik, Elizabeth S. Burnside

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

3. Mammogram Radiologist Routine screening No Radiology Report Abnormal finding? Yes Biopsy Benign Malignant Cancer InvasiveIn Situ

4. Biopsy Biopsy: – Costly – Invasive – Potentially painful Models based on mammography report and personal data help identify pre-biopsy cancer stage.

5. Cancer Stages: In Situ Cancer cells localized Did not spread Basement Membrane Abnormal cells

6. Cancer Stages: Invasive Cancer cells break through basement membrane Invade surrounding tissue Basement Membrane Abnormal cells

7. Treatment In Situ: – Can develop into Invasive – Excellent prognosis, less intensive treatment  Treat it “Overdiagnosis” (unnecessary treatment) Time to spread may be long – Patient may die of other causes

8. Problem Constraints Identify patient subgroups that would benefit most from treatment Use biopsy alternatives (like follow-up) Help patients make informed decisions, personalized medicine

9. Task Formulation Given: – Radiology reports – No biopsy Do: – Identify patient subgroups – Specify Invasive/In Situ probabilities

10. Data Radiology Report - Personal/family history - BIRADS code - Palpable lump - Mass specs - Calcifications... Match mammograms to biopsies 1063 Invasive, 412 In Situ cases Biopsy - Date - Breast side - Cancer stage...

11. Acronyms BI-RADS code: Breast Imaging Reporting and Data System – A number (0-6) summarizing the radiologist opinion and findings concerning a mammogram. – In increasing probability of malignancy: 1<2<3<0<4<5<6 DCIS: Ductal Carcinoma In Situ – One [and only?] type of In Situ Breast Cancer

12. Age Matters Apply Logistic Regression – Different attributes predict cancer stages in different age groups Stratify data (~menopausal status): – Older cohort (age => 65) (post-) – Middle cohort (50 <= age < 65) (peri-) – Younger cohort (age < 50) (pre-)

13. Age-Specific Attributes Find accurate age-specific attributes Inductive Logic Programming (ILP) confers added benefits beyond Logistic Regression: – Human comprehensible rules – Specific data subsets

14. Inductive Logic Programming Machine learning approach White-box classifier Constructs if-then rules Allows user interaction using background knowledge Operates on relational datasets

15. Example Assign mammograms to biopsies Discard: Record 11 since benign Stages since target of prediction Non-relational learner extracts: – BIRADS(10,100,5) – BIRADS(12,100,4) RecordPatientDateBIRADS 1010008/20105 1110002/20083 1220006/20094 PatientDateStage 10009/2010Invasive 10003/2008Benign 20007/2009In Situ

16. Link patients records, e.g: – Old study (id, old id) – Old biopsy (id, old id, result) – Access old study/biopsy attributes Compare attributes, e.g: – Mass size decrease (id, old id) – This-side breast BIRADS code increase (id, old id) ILP Predicate Invention

17. Example Cont’d Link records: OldStudy(10,11) Access previous study predicates: – BIRADS(11,100,3) – OldBiopsy(10,11,Benign) Compare predicates: – BIRADSincrease(10,11,3) RecordPatientDateBIRADS 1010008/20105 1110002/20083 1220006/20094 PatientDateStage 10009/2010Invasive 10003/2008Benign 20007/2009In Situ

18. Methodology Older Cohort Reports ILP Classifier Invasive v/s In Situ Rules Younger Cohort Reports Differential Prediction Older-Specific Invasive/In Situ Rules

19. Differential Prediction Limit to Older and Younger: – Maximize age and attribute difference – Leave-out peri-menopausal Define Invasive rules in Older: – Good Invasive prediction on older Precision > 60%, Recall > 10% – And significantly worse prediction on younger Precision difference p-value < 0.05

20. Invasive Rules in Older 1.The mammogram has a palpable lump in this- side breast. 2.The mammogram's indication for exam is “palpable lump”. 3.The mammogram's indication for exam is “palpable lump", and its other side BI-RADS < 3, and its mass margin is not reported.

21. Palpable Lump Higher occurrence in Younger Tendency in younger: – Rapid proliferation – Poor differentiation – In Situ thus more likely to be palpable Tendency in older: – Slow growth – When big enough to be palpable, almost certainly Invasive

22. Invasive Rules in Older Cont'd 1.The mammogram has an old-biopsy that was invasive 2.The mammogram has an old-biopsy that was invasive, and the biopsy happened within the same age group. Due to: – Longer life-span of older women – Higher recurrence of invasive tumors

23. In Situ Rules in Younger 1.The mammogram has a personal history of cancer in this-side breast, and this-side breast has a prior surgery, and its combined BI-RADS increased by at least 2 points compared to a previous study.

24. Recurrence A recurrence is a better predictor of In Situ in younger Contrast with previous rules, where invasive tumor recurrence is a better predictor of Invasive in older

25. Other Rules No rules met our criteria for: – In Situ in Older – Invasive in Younger Middle cohort behavior: – 2 rules like Older – 2 rules like Younger – 2 rules neither

26. Probabilities Rules Precision Older Precision Younger Recall Older Recall Younger Palpable 1 94%87%42%65% Palpable 2 95%86%35%62% Palpable 3 98%87%19%41% Invasive Biopsy 1 97%86%50%18% Invasive Biopsy 2 100%86%44%18% Younger Recurrence8%67%2%11%

27. Problem Solutions Identify patient subgroups that would benefit most from treatment => Rule coverage Use biopsy alternatives (like follow-up) => Pre-biopsy mammography report Help patients make informed decisions, personalized medicine => Assigning probabilities

28. Conclusion First differential predictive rules extraction method and application Personalized age-specific prediction New insight on: – Palpable lump – Recurrence