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-Madison2Radiology, UW-Madison

3Merck Research Labaratories4Biostatistics

5Computer Science, UW-Madison

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

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

• Primary modality of screening or diagnosis: Mammography

Age Stratification forInvasive vs In-situ Breast

Cancer

• Primary modality of detecting type of breast cancer: Biopsy

Age Stratification forInvasive 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%

• Invasive vs. DCIS distinction important because:

Age Stratification forInvasive vs In-situ Breast

Cancer

• 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

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LOGISTIC REGRESSION

ROC Curves

Purpose and Methods Contd.

Markov Decision Processes

Risk Assessment Tools

Clinical Implications

Clinical Implications

ROC & PR Curves, Statistical Testing

Structure of Data UsedNMDNational

Mammography Database Format

Free TextStructured

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

• 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

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 Distortiono Calcifications o Asymmetry (one view) o Focal asymmetry (two views) o Developing asymmetry o Mass o Single Dilated Ducto 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,

SpiculatedMass Shape Irregular, Lobular, Oval, RoundMass Density Fat Containing, Low Density, Equal Density, High Density

Mass Size <10mm, >10mm and <20mm, >20mm and<50mm,

>50mm

Special Cases Intra-mammary Lymph Node, Tubular Density,

Assymetric Breast Tissue, Focal Asymmetric DensityBiopsy Insitu, Invasive

Free Text

==Features

Extracted Using NLP

• 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

• 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

Methods: Performing Logistic Regression

P(Invasive|Demographic Factors, Mammographic Descriptors)

Methods: Validation Technique

n fold cross-validation Leave-one-out

Test fold

Training fold

1 32 n…

…

Data set

Fold

Merge tested folds for performance analysis

1 32 n…

Fold

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 + a b

Test - c d

Sensitivity=a/(a+c)

Specificity = d/(b+d)

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 Labels Subject Count Actual Invasive Count

AgeLT50 374 264Age5064 568 398

AgeGTE65 533 401Overall 1475 1063

Results: LRyoung vs. LRold

• Difference in AUC = 0.07

• Significant at p-value = 0.045

Results: LRyoung vs. LRold

• Improvement is in False Negatives

Results: LRyoung vs. LRold

• 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

Future Work

Markov Decision Processes

Risk Assessment Tools

Clinical Implications

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

THANK YOU!

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