Predicting Breast Cancer

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We looked at how computer-aided diagnostic tools can assist in increasing the specificity and sensitivity of mammogram interpretations, avoiding unnecessary biopsies.

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  • Predicting Breast Cancer

    1. 1. PredictingBreast Cancer Max Mitchell Amanda Rollason Cheannette Smith
    2. 2. AGENDA Understanding the Problem Exploring the Data  Frequency Tables  Graphs Modeling with Logistic Regression Validating the Predictive Model
    3. 3. THE PROBLEMOf women who have a mammogram interpretationthat leads to a breast biopsy, 70% actually havebenign outcomes, which means only 30% sensitivity. Understand Explore Model Validate
    4. 4. A SOLUTIONBetter diagnostic tools for physicians would helpincrease the sensitivity and specificity ofmammogram interpretations, while reducing thenumber of unnecessary breast biopsies. Understand Explore Model Validate
    5. 5. THE DATA SOURCE Dr. Rudiger Schulz-Wendtland, M. Elter, and T. Wittenberg at the Institute of Radiology at University of Erlangen in Nuremberg, Germany Data collected between 2003-2006 Published in Medical Physics in 2007: “The Prediction of Breast Cancer Biopsy Outcomes Using Two CAD Approaches that both Emphasize an Intelligible Decision Process” Donated database to University of California-Irvine for their machine learning database repository Understand Explore Model Validate
    6. 6. THE CASE-CONTROL STUDY 961 observations represent the outcome for women who already had breast biopsies  516 benign cases  445 malignant cases Two physicians reviewed the mammograms  not knowing that each woman already had a biopsy for the suspected mass  not knowing the outcome of her biopsy Computer aided-diagnosis (CAD) systems were run for the full-field digital mammograms Understand Explore Model Validate
    7. 7. THE VARIABLES 1 binary response, the SEVERITY of mass lesion  Malignant = 1  Benign = 0 1 continuous predictor, AGE 1 semi-ordinal predictor, the BI-RADS Assessment 3 nominal predictors, the CAD output  SHAPE  DENSITY  MARGIN Understand Explore Model Validate
    8. 8. THE MAMMOGRAM DATA Understand Explore Model Validate
    9. 9. Breast Imaging Reporting and Data System BI-RADS ASSESSMENT CLINICAL RECOMMENDATIONCATEGORY Assessment Need to review prior studies 0 Incomplete and/or complete additional imaging 1 Negative Continue routine screening 2 Benign Continue routine screening Probably Benign Short-term follow-up at 6 months, 3 Finding then every 6 to 12 months for 1 to 2 years 4 Suspicious Abnormality Perform biopsy, preferably needle biopsy Highly Suspicious of 5 Biopsy and treatment, as necessary Malignancy Known Biopsy– 6 Assure that treatment is completed Proven Malignancy Understand Explore Model Validate
    10. 10. SHAPE MARGIN1 = ROUND 1 = CIRCUMSCRIBED2 = OVAL 2 = MICROLOBULATED3 = LOBULAR 3 = OBSCURED4 = IRREGULAR 4 = ILL-DEFINED 5 = SPICULATED Understand Explore Model Validate
    11. 11. DENSITY1 = high 2 = iso 4=fat- containing 3=low Understand Explore Model Validate
    12. 12. EXPLORING THE DATA Understand Explore Model Validate
    13. 13. EXPLORING THE DATA Understand Explore Model Validate
    14. 14. EXPLORING THE DATA Understand Explore Model Validate
    15. 15. LOGISTIC REGRESSION MODELS There is a binary response variable. There are more than three predictors, so frequency tables alone will be inadequate. The predictors are both numerical and categorical. Some of the categorical variables are ordinal. Understand Explore Model Validate
    16. 16. PRE-MODELINGMODEL MAIN EFFECTS – Mostly Categorial 1 AGE BIRADSc SHAPEc MARGINc DENSITYc 2 AGE BIRADSc 3 AGE SHAPEc MARGINc DENSITYc 4 AGE SHAPEc MARGINc 5 AGE BIRADSc SHAPEc MARGINc 6 AGE BIRADSc SHAPEc Understand Explore Model Validate
    17. 17. PRE-MODELINGMODEL MAIN EFFECTS – Mostly Numerical 7 AGE BIRADS SHAPE MARGIN DENSITYc 8 AGE BIRADS 9 AGE SHAPE MARGIN DENSITYc 10 AGE SHAPE MARGIN 11 AGE BIRADS SHAPE MARGIN 12 AGE BIRADS SHAPE Understand Explore Model Validate
    18. 18. MODELING THE DATAMODEL TERMS A AGE BIRADSc SHAPEc MARGINc DENSITYc B AGE BIRADSc SHAPEc MARGINc C AGE BIRADSc SHAPEc D AGE BIRADS SHAPE E AGE BIRADS SHAPE AGE×BIRADS SHAPE×BIRADS Understand Explore Model Validate
    19. 19. LOGISTIC REGRESSION – Training MODEL p-cutoff Sensitivity Specificity AIC AUC = cA B c S c Mc D c 0.414 0.845 0.838 475.3 0.910 QCS A Bc S c M c 0.363 0.878 0.815 507.6 0.906 A Bc Sc 0.364 0.875 0.815 534.7 0.902 ABS 0.419 0.844 0.813 563.4 0.888A B S AB SB 0.438 0.873 0.809 544.0 0.899 Understand Explore Model Validate
    20. 20. LOGISTIC REGRESSIONLogit ( ˆ) α β1 AGE β2BIRADS β3SHAPE β4 AGE BIRADS β5BIRADS SHAPE Understand Explore Model Validate
    21. 21. LOGISTIC REGRESSION – Validation MODEL Sensitivity Specificity AUC 95% CI A Bc S c M c D c 0.866 0.863 (0.822, 0.907) QCS A Bc S c Mc 0.858 0.850 (0.812 ,0.897) A Bc S c 0.846 0.844 (0.802, 0.887) ABS 0.835 0.848 (0.798, 0.884) A B S AB SB 0.816 0.870 (0.800, 0.928) Understand Explore Model Validate
    22. 22. Comparing ROC curves Understand Explore Model Validate
    23. 23. Example 1AGE = 42 | BIRADS = 2 | SHAPE = Oval = 2Logit = -34.1514 + 0.2398(42) + 6.8365(2) + 4.0423 (2) – 0.0441(42)(2) – 0.7842(2)(2) = -8.903Odds = e-8.903 = 0.0001 Patient most likely does not have a malignant lesion.TRUE. She had multiple cutaneous neurofibromas. Theyare benign, so there is no evidence of malignancy. Thereader recommended that she should have a normalinterval screening follow-up in 12 months. Understand Explore Model Validate
    24. 24. Example 2 AGE = 62 | BIRADS = 4 | SHAPE = Irregular = 4 Logit = -34.1514 + 0.2398(62) + 6.8365(4) + 4.0423 (4) – 0.0441(62)(4) – 0.7842(4)(4) = 1.5162 Odds = e1.5162 = 4.55  Patient most likely does have a malignant lesion. TRUE. She had invasive ductal carcinoma, so there was evidence of malignancy. The reader saw that she had a suspicious abnormality and recommended a core needle biopsy. Understand Explore Model Validate
    25. 25. Conclusion Readers’ interpretation alone (BIRADS) isn’t sufficient. Computer Aided-Diagnosis systems (SHAPE, MARGIN, and DENSITY) alone aren’t sufficient. AGE does need to be considered for determining if a breast biopsy is warranted. AGE, BIRADS, and SHAPE did the most to improve sensitivity and specificity. Understand Explore Model Validate
    26. 26. For the Future Incorporate other CAD tools.  MRI tests  Ultrasound examinations Explore results of other modeling methods.  Decision Trees  Boot-strapping Educate patients regarding the imperfect process of mammogram interpretation. Understand Explore Model Validate
    27. 27. Questions?Understand Explore Model Validate

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