1. Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal 3, Mary Lindstrom 4,Houssam Nassif 5,Elizabeth S Burnside 2 1 Industrial and Systems Engineering, UW-Madison 2 Radiology, UW-Madison 3 Merck Research Labaratories 4 Biostatistics 5 Computer Science, UW-Madison

2. Breast Anatomy Breast profile: – A ducts – B lobules – C dilated section of duct to hold milk – D nipple – E fat – F pectoralis major muscle – G chest wall/rib cage Enlargement: – A normal duct cells – B basement membrane – C lumen (center of duct) www.breastcancer.org

3. Age Stratified Risk Prediction of Invasive vs In-situ Breast Cancer: A Logistic Regression Model Progression of Breast Cancer Normal duct Typical ductal hyperplasia Atypical ductal hyperplasia Ductal carcinoma in situ (DCIS) Invasive ductal carcinoma

4. Primary modality of screening or diagnosis: Mammography Age Stratification for Invasive vs In-situ Breast Cancer Performs differently in different age groups Sensitivity: Age – <40  54% – 40-49  77% – 50-65  78% – >65  81% Sensitivity: Breast Density – 68% vs. 85% – Younger vs. older

5. Primary modality of detecting type of breast cancer: Biopsy Age Stratification for Invasive vs In-situ Breast Cancer Incidence of DCIS has increased since adoption of mammography DCIS has favorable prognosis: will often not cause mortality for years PPV of biopsy 20%

6. Invasive vs. DCIS distinction important because: Age Stratification for Invasive vs In-situ Breast Cancer Requires different treatment Life expectancy difference in older and younger women – Over diagnosis which does not correspond to reduced mortality – Breast cancer less aggressive in older women – Invasive procedures more risky in older women – Resources could be better spent on more serious co-morbidities

7. Develop a risk prediction model for prospective differentiation of DCIS versus invasive breast cancer Measure and compare model performance for different age groups Purpose and Methods LOGISTIC REGRESSION ROC Curves

8. Purpose and Methods Contd. Markov Decision Processes Risk Assessment Tools Clinical Implications ROC & PR Curves, Statistical Testing

9. Structure of Data Used NMD National Mammography Database Format Free Text Structured Demographic Factors Mammographic Descriptors Radiologists’ Overall Assessment of the Mammogram with Some Repeat to the Structured Part BIRADS descriptors Turned into Structured format using Natural Language Processing

10. Information retrieval from free text given a standardized lexicon Parse sentences to detect BIRADS descriptors using Natural Language Processing in PERL Test on a set of 100 which is manually populated – 97.7% Precision – 95.5% Recall Methods: Processing Free Text

11. Data in Detail Structured DEMOGRAPHIC  Age  Family History  Personal History  Prior Surgery  Palpable Lump  Interpreting Radiologist  Breast Density  Indication for Exam if Diagnostic MAMMOGRAPHIC  BI-RADS Assessment  Principle Abnormal Finding o Architectural Distortion o Calcifications o Asymmetry (one view) o Focal asymmetry (two views) o Developing asymmetry o Mass o Single Dilated Duct o Both Calcifications and Something Else Associated Findings  Skin Retraction, Nipple Retraction, Skin Thickening, Trabecular Thickening, Skin Lesion, Axillary Adenopathy, Architectural Distortion Calcification Distribution  Clustered, Linear, Regional, Scattered, Segmental, Diffuse Calcification Morphology  Pleomorphic, Dystrophic, Lucent, Punctate, Vascular, Eggshell, Dermal, Fine-Linear, Round, Popcorn, Milk of Calcium, Rod Like, Suture, Amorphous, Mass Margins  Circumscribed, Obscured, Defined, Microlobulated, Spiculated Mass Shape  Irregular, Lobular, Oval, Round Mass Density  Fat Containing, Low Density, Equal Density, High Density Mass Size  10mm and 20mm and 50mm Special Cases  Intra-mammary Lymph Node, Tubular Density, Assymetric Breast Tissue, Focal Asymmetric Density Biopsy  Insitu, Invasive Free Text == Features Extracted Using NLP

12. 1475 Diagnostic Mammograms  1378 Patients – 1298 patients with single mammogram – 81 patients with 2 mammograms – 5 patients with three mammograms 1063 cases invasive vs. 412 DCIS Age range  27 to 97 with – Mean 59.7 and standard deviation 13.4 Summary of Data

13. Regress with a dichotomous outcome, where the patient is known to have malignant condition, i.e. – Invasive or – DCIS Stratified data into 3 groups – Overall Model  LR 1475 records – Age Less Than 50  LR young 374 records – Age Greater Than 65  LR Old 533 r ecords Used stepwise regression to find the appropriate models. Possibility of interactions were investigated Methods: Performing Logistic Regression P(Invasive|Demographic Factors, Mammographic Descriptors)

14. Methods: Validation Technique  n fold cross-validation  Leave-one-out Test fold Training fold 132n … … Data set Fold   Merge tested folds for performance analysis 132 n … Fold  

15. Sensitivity vs. 1-Specificity at all thresholds – Sensitivity: True Positive Rate – Specificity: True Negative Rate – Thresholds: Probability above which call “Invasive” – AUC: Area Under the Curve Methods: Measuring Performance Patient with Disease Patients without disease Test +ab Test -cd Sensitivity=a/(a+c) Specificity = d/(b+d)

16. Results: LR Overall model significant at p- value<0.01 Not enough power to justify inclusion of interaction terms (Over-fitting) Acceptable ROC Decreasing trend in Error rates Row LabelsSubject CountActual Invasive Count AgeLT50374264 Age5064568398 AgeGTE65533401 Overall14751063

17. Results: LR young vs. LR old Risk FactorOlderYounger OddsP-ValueOddsP-Value Significant in Both Models Palpable Lump>10.01>1<0.001 Mass Shape~>10.03~>10.03 Principal Finding>1<0.001>1<0.001 Significant in Only Young Architectural Distortion >10.06 Mass SizeVaries0.05 Significant in Only Old Family History Varies0.04 Calcification Distribution <10.01 Focal Asymmetry>10.08 Prior Surgery >10.13

18. Difference in AUC = 0.07 Significant at p-value = 0.045 Results: LR young vs. LR old

19. Improvement is in False Negatives Results: LR young vs. LR old

20. Mammography is not perfect and performs better in older women. There is a need for discriminating between invasive and DCIS to better manage the breast disease in the context of age and other comorbidities An age based risk prediction model for assessing performance difference in discriminating invasive vs. DCIS is necessary Such a model would enable physicians to make more informed decisions Demonstration of performance difference and varying risk factors in different age cohorts justifies In Summary

21. Future Work Markov Decision Processes Risk Assessment Tools Clinical Implications ROC & PR Curves, Statistical Testing Get in Literature Using POMDPs to Determine the Optimal Mammography Screening Schedule From the Patient's Perspective Presenting Author: Turgay Ayer,University of Wisconsin Co-Author: Oguzhan Alagoz,Assistant Professor, University of Wisconsin-Madison

22. THANK YOU! Questions?