age stratified risk prediction of invasive versus in-situ breast cancer: a logistic regression model...
TRANSCRIPT
![Page 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](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/1.jpg)
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
![Page 2: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/2.jpg)
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
![Page 3: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/3.jpg)
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
![Page 4: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/4.jpg)
• Primary modality of screening or diagnosis: Mammography
Age Stratification forInvasive vs In-situ Breast
Cancer
![Page 5: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/5.jpg)
• 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%
![Page 6: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/6.jpg)
• Invasive vs. DCIS distinction important because:
Age Stratification forInvasive vs In-situ Breast
Cancer
![Page 7: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/7.jpg)
• 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
)(11
1
N
iiiX
e
LOGISTIC REGRESSION
ROC Curves
![Page 8: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/8.jpg)
Purpose and Methods Contd.
Markov Decision Processes
Risk Assessment Tools
Clinical Implications
Clinical Implications
ROC & PR Curves, Statistical Testing
![Page 9: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/9.jpg)
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
![Page 10: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/10.jpg)
• 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
![Page 11: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/11.jpg)
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
![Page 12: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/12.jpg)
• 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
![Page 13: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/13.jpg)
• 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)
![Page 14: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/14.jpg)
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
![Page 15: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/15.jpg)
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)
![Page 16: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/16.jpg)
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
![Page 17: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/17.jpg)
Results: LRyoung vs. LRold
![Page 18: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/18.jpg)
• Difference in AUC = 0.07
• Significant at p-value = 0.045
Results: LRyoung vs. LRold
![Page 19: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/19.jpg)
• Improvement is in False Negatives
Results: LRyoung vs. LRold
![Page 20: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/20.jpg)
• 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
![Page 21: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/21.jpg)
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
![Page 22: Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model Mehmet Ayvaci 1,2 Oguzhan Alagoz 1,Jagpreet Chhatwal](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649e885503460f94b8c550/html5/thumbnails/22.jpg)
THANK YOU!
Questions?