Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model

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Age Stratified Risk Prediction of Invasive versus
In-situ Breast Cancer A Logistic Regression
Model

• Mehmet Ayvaci1,2
• Oguzhan Alagoz1,Jagpreet Chhatwal3,
• Mary Lindstrom4,Houssam Nassif5,Elizabeth S
  Burnside2

1Industrial and Systems Engineering,
UW-Madison 2Radiology, UW-Madison 3Merck Research
Labaratories 4Biostatistics 5Computer Science,
UW-Madison
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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
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Progression of Breast Cancer
Atypical ductal hyperplasia
Normal duct
Invasive ductal carcinoma
Typical ductal hyperplasia
Ductal carcinoma in situ (DCIS)

• Age Stratified Risk Prediction of
• Invasive vs In-situ Breast Cancer
• A Logistic Regression Model

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Age Stratification forInvasive vs In-situ Breast
Cancer

• Primary modality of screening or diagnosis
  Mammography

• Performs differently in different age groups
• Sensitivity Age
• lt40 ? 54
• 40-49 ? 77
• 50-65 ? 78
• gt65 ? 81
• Sensitivity Breast Density
• 68 vs. 85
• Younger vs. older

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Age Stratification forInvasive vs In-situ Breast
Cancer

• Primary modality of detecting type of breast
  cancer Biopsy

• Incidence of DCIS has increased since adoption of
  mammography
• DCIS has favorable prognosis will often not
  cause mortality for years
• PPV of biopsy 20

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Age Stratification forInvasive vs In-situ Breast
Cancer

• Invasive vs. DCIS distinction important because

• 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

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Purpose and Methods

• Develop a risk prediction model for prospective
  differentiation of DCIS versus invasive breast
  cancer
• Measure and compare model performance for
  different age groups

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Purpose and Methods Contd.
Measure Risk Of Invasive Cancer Given Information
Risk Assessment Tools
Clinical Implications
Validation of The Model
ROC PR Curves, Statistical Testing
Optimize Sequential Decision Making in the
Context Of Breast Cancer Screening
Markov Decision Processes
Clinical Implications
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Structure of Data Used
NMD National Mammography Database Format
Radiologists Overall Assessment of the
Mammogram with Some Repeat to the Structured Part
Free Text
Structured
Demographic Factors
Mammographic Descriptors
Turned into Structured format using Natural
Language Processing
BIRADS descriptors
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Methods Processing Free Text

• 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

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Data in Detail
Free Text
Features Extracted Using NLP
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Summary of Data

• 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

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Methods Performing Logistic Regression

• 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 ? LRyoung 374 records
• Age Greater Than 65 ? LROld 533 records
• Used stepwise regression to find the appropriate
  models. Possibility of interactions were
  investigated

P(InvasiveDemographic Factors, Mammographic
Descriptors)
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Methods Validation Technique

• n fold cross-validation
• Leave-one-out

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Methods Measuring Performance

• 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

Sensitivitya/(ac) Specificity d/(bd)
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Results LR

• Overall model significant at p-valuelt0.01
• Not enough power to justify inclusion of
  interaction terms (Over-fitting)
• Acceptable ROC
• Decreasing trend in Error rates

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Results LRyoung vs. LRold
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Results LRyoung vs. LRold

• Difference in AUC 0.07
• Significant at p-value 0.045

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Results LRyoung vs. LRold

• Improvement is in False Negatives

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In Summary

• 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

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Future Work
Measure Risk Of Invasive Cancer Given Information
Risk Assessment Tools
Clinical Implications
Validation of The Model
ROC PR Curves, Statistical Testing
Get in Literature
Markov Decision Processes
Optimize Sequential Decision Making in the
Context Of Breast Cancer Screening
Clinical Implications
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
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Questions?

• THANK YOU!