Presented by DanniseJangyoung

MarcusBongsoo

Breast Cancer Diagnostics

Outline

Introduction SVM Logistic Regression Conclusion & Discussion

Introduction

Epidemiology World wide the second most common

cancer 1.3 million cases

Most common type of cancer in women US (2009) approximately 40,170

women were expected to die from breast cancer

Most common in well developed countries

Strongly related to age

World Health Organization, American Cancer Society

Introduction Cancer that forms in tissues of the

breast, usually ducts and lobules Diagnosis: mammogram, FNA or

surgical biopsy to identify the nature of the mass

Normal breast Breast cancer

Fine Needle Aspiration

Surgical biopsy

Introduction Benign and malignant tumors

Benign: cyst or other disease Malignant: cancer

Goal: To reduce the number of predictors classifying tumors to simplify diagnosis

Data characteristics radius texture perimeter area smoothness compactness concavity Concave points symmetry Fractal dimension

Mangasarian, et al (1994)

Wolberg, et al. (1994)

SVM (Support vector machine)

Breast cancer Wisconsin data set (569*32)

Linearly separable (Benign & Malignant )

SVM

only means model ( 3-12)

Benign 99.43 %

Malignant 97.63%

SVM

cross-validation of the model - fit a model with 80% of the rows, check if it can predict the type of the other 20% of rows

Benign 94.80% Malignant 91.66%

Logistic Regression

Reduce the number of predictors Simplify the diagnosis Less measurements, less time, less

cost

Logistic Regression Estimate Std. Error z value Pr(>|z|)

(Intercept) 7.35952 12.85259 0.573 0.5669

radius 2.04930 3.71588 0.551 0.5813

texture -0.38473 0.06454 -5.961 2.5e-09 ***

perimeter 0.07151 0.50516 0.142 0.8874

area -0.03980 0.01674 -2.377 0.0174 *

smoothness -76.43227 31.95492 -2.392 0.0168 *

compactness 1.46242 20.34249 0.072 0.9427

concavity -8.46870 8.12003 -1.043 0.2970

concave_points -66.82176 28.52910 -2.342 0.0192 *

symmetry -16.27824 10.63059 -1.531 0.1257

fractal_dimension 68.33703 85.55666 0.799 0.4244

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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

SVM

Perform SVM again Used predictors: texture, area,

smoothness, and concave points To assure the validity of the model,

we fit it to 80% of the data and make predictions about the remaining 20%

SVM Results

Full dataset

Bootstrap

Type Benign Malign

Correct (%) 96.92 90.57

Type Benign Malign

Correct (%) 96.63 89.85

Conclusion & Discussion

Type Benign MalignMean model 99.43 97.63Cross Validation(80%) 94.80 91.66The reduced model (Full Dataset) 96.62 90.57The reduced model (Bootstrap) 96.63 89.85

Summary table

Conclusion & Discussion

The characteristics of cells are key to diagnose malign of breast cancer

SVM was good to validate diagnostic model

The reduced model is quiet accurate, and it will help doctors to save the cost and efforts of diagnostics

Conclusion & Discussion

Cell line Origin of cell

Estrogen receptors

Progesterone receptors

ERBB2Amplification

BT-20 Primary No No NoBT-474 Primary Yes Yes YesMCF-7 Metastasis Yes Yes NoSK-BR-3 Metastasis No No Yes

Treatment is based on the diagnostics of cell lines (Examples of invasive ductal carcinoma)

Lasfargues, EY et al. 1958. Cultivation of human breast carcinomas. Borras, M et al. 1997. Estrogen receptor negative/progesterone receptor-positive evsa-T mammary tumor cells: a model for assessing the biological property of this peculiar phenotype of breast cancers.

Conclusion & Discussion

Current breast cancer researches and diagnostics by 3D pictures

(Dr. Mina J. Bissell, Lawrence Berkeley National Laboratory)

Britta Weigelt, Mina J. Bissell. 2008 Unraveling the microenvironmental influences on the normal mammary gland and breast cancer. Seminar in Cancer Biology. (18) 311-321

Thank you very much !Any questions ?