Age Stratified Risk Prediction of Invasive versus In-situ Breast .ppt

1、Age Stratified Risk Prediction of Invasive versus In-situ Breast Cancer: A Logistic Regression Model,Mehmet Ayvaci1,2Oguzhan Alagoz1,Jagpreet Chhatwal3, Mary Lindstrom4,Houssam Nassif5,Elizabeth S Burnside2,1Industrial and Systems Engineering, UW-Madison 2Radiology, UW-Madison 3Merck Research Labara

2、tories 4Biostatistics 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.or

3、g,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: Mam

4、mography,Age Stratification for Invasive vs In-situ Breast Cancer,Performs differently in different age groups Sensitivity: Age 65 81% Sensitivity: Breast Density 68% vs. 85% Younger vs. older,Primary modality of detecting type of breast cancer: Biopsy,Age Stratification for Invasive vs In-situ Brea

5、st 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 for Invasive vs In-situ Breast Cancer,Requires different treatment Life

6、 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,Develop a risk prediction model fo

7、r prospective differentiation of DCIS versus invasive breast cancerMeasure and compare model performance for different age groups,Purpose and Methods,Purpose and Methods Contd.,Measure Risk Of Invasive Cancer Given Information,Validation of The Model,Optimize Sequential Decision Making in the Contex

8、t Of Breast Cancer Screening,Markov Decision Processes,Risk Assessment Tools,Clinical Implications,Clinical Implications,ROC & PR Curves, Statistical Testing,Structure of Data Used,NMD National Mammography Database Format,Free Text,Structured,Demographic Factors,Mammographic Descriptors,Radiologists

9、 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 Proces

10、sing 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,Free Text,= Features Extracted Using NLP,1475 Diagnostic Mammograms 1378 Patients 1298 patients with single mammogram 81 patients with 2 mammograms 5 patients with

11、three mammograms1063 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 DCISStratified data into 3 groups Overall Model LR 1475 records

12、Age Less Than 50 LRyoung 374 records Age Greater Than 65 LROld 533 recordsUsed stepwise regression to find the appropriate models. Possibility of interactions were investigated,Methods: Performing Logistic Regression,P(Invasive|Demographic Factors, Mammographic Descriptors),Methods: Validation Techn

13、ique,n fold cross-validation Leave-one-out,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,Sensitivity=a/(a+c)Specificity = d/(

14、b+d),Results: LR,Overall model significant at p-value0.01 Not enough power to justify inclusion of interaction terms (Over-fitting) Acceptable ROC Decreasing trend in Error rates,Results: LRyoung vs. LRold,Difference in AUC = 0.07 Significant at p-value = 0.045,Results: LRyoung vs. LRold,Improvement

15、 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 asses

16、sing 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,Measure Risk Of Invasive Cancer

17、 Given Information,Validation of The Model,Optimize Sequential Decision Making in the Context Of Breast Cancer Screening,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 Patients Perspective Presenting Author: Turgay Ayer,University of Wisconsin Co-Author: Oguzhan Alagoz,Assistant Professor, University of Wisconsin-Madison,THANK YOU!,Questions?,