Predicting the Malignant Breast Cancer using Tumor Tissue Features

Wenrui Zhao

College of Art and Science, the Ohio State University, Columbus, OH, 43210, U.S.A.

Keywords: Breast Cancer, Breast Cancer Datase, Feature Selection, FNA, Cancer Diagnosis.

Abstract: Breast cancer is one of the most common cancers in women and is the second leading cause of death after

lung cancer. In clinical diagnosis, fine needle aspiration cytology is often used in tumor diagnosis, considering

safety, accuracy, and ease of operation. Pathologists can judge whether the patient's tumor tissue is malignant

by observing the cell population. The accuracy of fine-needle biopsy largely depends on the doctors who

participate in sampling and analysis. Therefore, it is crucial to study which characteristics of cells can become

a solid basis for discrimination. This article constructs univariate and multivariate logistic regression models

to analyze the predictive value of 9 features of the cell to breast cancer. By evaluating the ROC curve, the

article shows that the constructed model accurately predicts malignant tumor tissue. The 9 characteristics of

FNA quantitative detection of tumor tissue are of great value in predicting malignant breast cancer.

1 INTRODUCTION

Breast cancer is one of the most common cancers in

women and is the second leading cause of death after

lung cancer (Nguyen,1970) (Mangasarian, 1990). In

2020, over 2.3 million women were diagnosed with

breast cancer worldwide, and 685 thousand died. Due

to population growth, aging, and the increasing

prevalence of known cancer risk factors (such as

smoking and unhealthy eating), WHO believes that if

the global incidence rate remains the same as in 2020,

there will be around 28.4 million new cancer cases

worldwide in 2040. Women in every country face the

risks of developing breast cancer at any age after

puberty, but the incidence rate will increase with age

growth (Piro,2021). Existing diagnostic techniques,

including nuclear magnetic resonance imaging,

ultrasound, CT (computer tomography) or PET

(positron emission tomography), are very effective in

tumor detection (World Health Organization, 2021)

However, when doctors find suspicious tumor tissue,

they still hope to obtain tissue samples for analysis.

Biopsy isan essential technique for the diagnosis of

cancer in the clinic. Because fine needle biopsy does

not need any preparation in advance, nor does it need

special dietary norms, fine-needle aspiration (FNA)

has become the preliminary diagnostic basis for

judging whether breast tissue is cancerous. A large

number of data show that although FNA has many

advantages, a few cases may be misdiagnosed.

Therefore, it is vital to study which characteristics of

cells can become a solid basis for discrimination.

From 1989 to 1991, Dr. Wolberg, Dr. Mangasarian

and two graduate students constructed a classifier

using the pattern separation multi-surface method

(MSM) for these nine features and successfully

diagnosed 97% of new cases (Nguyen,1970)

(Wolberg,1989). These led to the Wisconsin breast

cancer dataset. This article constructs univariate and

multivariate logistic regression models to analyze the

predictive value of 9 features of the cell to breast

cancer. This article used biometric methods for

exploratory data analysis to focus more narrowly on

checking the fitting degree of the model (Chatfield,

2021) By studying the different importance of the 9

features of cells, the article helps people establish a

more standard method to judge whether tumor tissue

is malignant.

2 ANALYSES

FNA uses a tiny needle tube of about 20-27G (similar

to or smaller than the needle tube for regular blood

testing. Generally, the larger the number of G, the

smaller the needle tube) (CancerQuest,2021). Due to

the small amount of tissue and its cellular

components collected, pathologists will pay more

attention to the observation of cell populations. The

study used the Wisconsin Breast Cancer Dataset

204

Zhao, W.

Predicting the Malignant Breast Cancer using Tumor Tissue Features.

DOI: 10.5220/0011196000003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 204-211

ISBN: 978-989-758-595-1

(WBCD) for women in the UCI machine learning

dataset (Nguyen, 1970) (Wolberg, 1989) (Taylor,

2021) (Tukey, 1992) The dataset contains 699

records. It includes nine features in this dataset.

Depending on the values of features, benign and

malignant masses can be distinguished. This dataset

has 16 missing values, this article discarded these

missing values in our experiment, and we considered

the remaining 683 records. 444 records belong to the

benign category from the cleaned data set, and the

remaining 239 records belong to the malignant

category. An important step in the breast cancer

diagnosis model is feature extraction. The optimum

feature set should have adequate and discriminating

features while mainly reducing the redundancy of

components space to avoid low sampling density.

This dataset provides nine crucial features of the cell

population, which are clump thickness, uniformity of

cell size, uniformity of cell shape, marginal adhesion,

single epithelial cell size, bare nuclei, bland

chromatin, normal nucleoli, and mitoses.

Exploratory data analysis begins with the

establishment of logistic regression models and

random forest. By observing the performance of the

model in each category through the visual confusion

matrix, the accuracy and recall of the model

corresponding to each class can be calculated.

Because judging the diagnosis result is a binary

classification problem, the ROC curve can analyze

the coordinates well. An essential feature of the ROC

curve is its area. An area of 0.5 is random

classification, and the recognition ability is 0. The

closer the area is to 1, the stronger the recognition

ability is, and an area equal to 1 is complete

recognition.

3 RESULTS

3.1

Exploratory Data Analysis

The diagnosis of malignant tumors belongs to binary

classification problems. In other words, for a patient,

her tumor result is either malignant or not. Therefore,

according to the data type, the results can be divided

into benign and malignant.

Exploratory data analysis (EDA) is an approach

for data analysis that employs a table to maximize

insight into the dataset and extract important

variables. Through EDA, figure 1 was derived. In the

table, the lower the p-value, the greater the statistical

significance of the observed difference. The result

shows that the nine characteristics of the cells we

observed are statistically significantly correlated with

the diagnostic results.

In addition, in 241 patients with malignant

tumors, the values of 9 characteristics of cells were

significantly higher than those in patients with benign

tumors, and the p-value of the statistical test was less

than 0.05 (< 0.0001), ndicating that these nine

variables are adverse predictors of malignant tumors.

Figure 1: Baseline characteristics of the study population

Correlation analysis is necessary before logistic

regression analysis. This article further discussed the

correlation between these nine variables and derived

Figure 2. It is found that these variables are positively

correlated, which is consistent with the information

prompted in Figure 1. Among them, judging by the

Predicting the Malignant Breast Cancer using Tumor Tissue Features

205

color depth of the chart, the uniformity of cell size

and cell shape has the strongest correlation, which is

closest to 1. Through correlation analysis, it is

suggested that all 9 features have good consistency in

describing the morphology of tumor cells.

Figure 2: Correlation between 9 Features.

3.2 Building Logistic Regression

Models

Because the number of cases given by the database is

not large, only nine factors appear in about 600

diagnoses. Therefore, if all elements are analyzed in

the equation, the results may be problematic

(Mangasarian,1990). In this case, univariate analysis

can help to screen out significant variables and then

put these variables into the equation for multivariate

analysis.

In the univariate logistic regression model, it can

be observed that every single factor is correlated with

tumor categories. For example, the results in clump

thickness (OR:2.55, 95%CI: [2.25,2.97]) indicates

that when the clump thickness of cells increases by

one unit, the probability that the tissue is malignant

increases by 1.55 times. All else follows: uniformity

of cell size (OR:4.43, 95%CI: [3.54,/70]), uniformity

of cell shape (OR:4.08, 95%CI: [3.32,5.17]),

marginal adhesion (OR:2.62, 95%CI: [2.26,3.11]),

single epithelial cell size (OR:3.89, 95%CI:

[3.17,4.89]), bare nuclei (OR:2.18, 95%CI:

[1.96,2.48]), bland chromatin (OR:3.75, 95%CI:

[3.06,4.74]), normal nucleoli (OR:2.36, 95%CI:

[2.06,2.76]), and mitoses (OR:3.84, 95%CI:

[2.78,5.62]).

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

206

Figure 3: Uni-variate Logistic Regression Model.

In multivariate analysis, only statistically

significant independent variables in the univariate

analysis may lead to some influencing factors not

having the opportunity to enter the multivariate

model. However, for the nine features studied in this

article, the P-value is far less than 0.05, so it is

feasible to incorporate all these factors into the

multivariable logistic regression model.

In this multivariable logistic regression model, for

example, when the other eight variables remain

constant, if the clump thickness of cells increases by

one unit, the probability of malignant tissue increases

by 0.71 times. All else follows: uniformity of cell size

(OR:100, 95%CI: [0.71,1.48]), uniformity of cell

shape (OR:1.41, 95%CI: [0.93, 2.11]), marginal

adhesion (OR:1.27, 95%CI: [1.02,1.60]), single

epithelial cell size (OR:1.07, 95%CI: [0.80,1.43]),

bare nuclei (OR:1.45, 95%CI: [1.24, 1.74]), bland

chromatin (OR:1.54, 95%CI: [1.15, 2.11]), normal

nucleoli (OR:1.15, 95%CI: [0.94, 1.41]), and mitoses

(OR:1.73, 95%CI: [1.02, 2.94]).

Figure 4: Multi-variate Logistic Regression Model.

3.3 Analysis from Confusion Matrix

In the confusion matrix, the amount of data is 698, in

which the row represents the prediction category of

the data and the column represents the real category.

There are 675 cases whose predicted category is

consistent with the real category, accounting for

96.7% of all cases. Therefore, the matrix indicates

that the prediction can correctly classify 96.7% of the

samples.

The model correctly predicts 443 data in the

benign category, and 232 data in the malignant

category. There are 14 data which the prediction is

benign, but the real category is malignant, and 9 data

which the prediction is malignant, but the real

category is benign. This shows that although FNC has

many advantages, there are still a few cases that may

be misdiagnosed. In this sample, the false negative is

greater than the false positive.

Variable OR 95%CI

fit1:Clump Thickness 2.55 [2.25,2.97]

fit2:Uniformity of cell size 4.43 [3.54,5.70]

fit3:Uniformity of Cell Shape 4.08 [3.32,5.17]

fit4:Marginal Adhesion 2.62 [2.26,3.11]

fit5:Single Epithelial Cell Size 3.89 [3.17,4.89]

fit6:Bare Nuclei 2.18 [1.96,2.48]

fit7:Bland Chromatin 3.75 [3.06,4.74]

fit8:Normal Nucleoli 2.36 [2.06,2.76]

fit9:Mitoses 3.84 [2.78,5.62]

Univariable Logistic regression analysis predicting Breast Cancer

Variable OR 95%CI

fit1:Clump Thickness 1.71 [1.35,2.26]

fit2:Uniformity of cell size 1.00 [0.71,1.48]

fit3:Uniformity of Cell Shape 1.41 [0.93,2.11]

fit4:Marginal Adhesion 1.27 [1.02,1.60]

fit5:Single Epithelial Cell Size 1.07 [0.80,1.43]

fit6:Bare Nuclei 1.45 [1.24,1.74]

fit7:Bland Chromatin 1.54 [1.15,2.11]

fit8:Normal Nucleoli 1.15 [0.94,1.41]

fit9:Mitoses 1.73 [1.02,2.94]

Multivariable Logistic regression analysis predicting Breast Cancer

Predicting the Malignant Breast Cancer using Tumor Tissue Features

207

Figure 5: Confusion Matrix for Multi-variable Regress Model.

3.4 Analysis from Evaluation Matrix

The accuracy rate represents the proportion of

correctly predicted samples in all samples, which is

0.9670487. The accuracy rate of the benign class

represents the proportion of samples whose real

category is benign among the samples predicted as

benign, which is 0.9541667; The recall rate of the

benign class represents the proportion of samples

successfully predicted by the model in the real benign

samples, which is 0. 7502075. The F-measure is the

harmonic mean of precision and recall. In most

situations, there would be a trade-off between

precision and recall. Since the F-Measure is

0.952183, which is pretty close to 1, the regression

analysis will give out both high recall and high

precision.

Figure 6: Evaluation Matrix.

3.5 Confusion Matrices’ Comparison

between the Multivariable Logistic

Regression Model and Random

Forest

The matrix for random forest correctly predicts 443

data in the benign category and 234 data in the

malignant category. There are 14 data which the

prediction is benign, but the real category is

malignant, and 7 data which the prediction is

malignant, but the real category is benign. There are

677 cases whose predicted category is consistent with

the real category, accounting for 96.9% of all cases.

Benign Malignant

Benign

443 14

Malignant

9 232

Actual Class

Predicted Class

Accuracy 0.9670487

Precision 0.9541667

Recall 0.9502075

F_Measure 0.952183

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

208

Since the confusion matrix for the random forest

is not significantly different from the multivariable

logistic regression model in Figure 5, considering the

interpretability of the multivariable regression model,

it would be better to use the multivariable logistic

regression model and evaluate its ROC curve.

Figure 7: Confusion Matrix for Random Forest.

3.6 Analysis of ROC Curve

The closer the ROC curve is to the upper left corner

(0,1) model on the image, the better, the larger the

area (AUC value) surrounded by the horizontal axis

and straight-line FPR = 1 under the ROC curve, the

better.

The area under the ROC curve, that is, the AUC,

is 0.995, which shows that the prediction effect of this

model for malignant tumor tissue is very accurate.

When setting the cut-off of the model output to 0.195,

the model can achieve its best outcome. Meanwhile,

the sensitivity of the model is 0.992, and its

specificity is 0.967.

Figure 8: ROC Curve.

3.7 Analysis of Random Forest

The Mean Decrease Accuracy plot expresses how

much accuracy the model losses by excluding each

variable. Bare Nuclei and clump thickness are

influential factors from the first diagram, which

means that these two variables are more important for

successful classification than the others. Whereas

under different standards, uniformity of cell size and

uniformity of cell shape become contributing factors

to the homogeneity of the nodes and leaves in the

resulting random forest.

Predicting the Malignant Breast Cancer using Tumor Tissue Features

209

Figure 9: Random Forest.

4 CONCLUSIONS

FNA is simple, fast and safe. Now it has become one

of the critical diagnostic methods of clinical diseases.

However, due to the small amount of tissue and its

cellular components, the tissue morphology and

interstitial structure in the specimen are mostly or

entirely lost, which cannot reflect the overall picture

of the type of lesion and interfere with the observation

of cell characteristics. Therefore, quantitative

detection of the 9 features of tumor tissues using FNA

technology is of great value in predicting malignant

breast cancer. Among them, clump thickness,

mitoses, and bland chromatin are of high predictive

value.

A larger sample size collected from over the

world or some certain nations instead of a state would

be always expected. It could improve the accuracy

and generality of the study.

A lack of personal information (i.e. age, family

history of breast cancer…) may impede the progress

of generalizing the conclusion. This information

would be helpful when applying our study to a border

sample, or in practical conditions.

REFERENCES

Biopsy. CancerQuest. (n.d.). Retrieved November 6, 2021,

from https://www.cancerquest.org/zh-

hans/geihuanzhe/jianceyuzhenduan/huotizuzhijiancha

zuzhi.

Chatfield, C. (n.d.). Problem solving: A statistician's guide,

second edition. in Search Works catalog. Retrieved

October 7, 2021.

Nguyen, Q. H., Do, T. T. T., Wang, Y., Heng, S. S., Chen,

K., Ang, W. H. M., Philip, C. E., Singh, M., Pham, H.

N., Nguyen, B. P., & Chua, M. C. H. (1970, January 1).

Breast cancer prediction using feature selection and

ensemble voting: Semantic scholar. undefined.

O. Mangasarian W. Wolberg, O. Mangasarian

Multisurface method of pattern separation for medical

diagnosis applied to breast cytology. Proceedings of the

National Academy of Sciences of the United States of

America.

Piro, M., Bona, R. D., Abbate, A., Biasucci, L. M., & Crea,

F. (2010, March 1). Sex-related differences in

myocardial remodeling: Journal of the American

College of Cardiology. Retrieved September 30, 2021,

from

https://www.jacc.org/doi/abs/10.1016/j.jacc.2009.09.0

65.

Robust linear programming discrimination of two linearly

inseparable sets. Taylor & Francis. (n.d.). Retrieved

October 1, 2021, from

https://www.tandfonline.com/doi/abs/10.1080/105567

89208805504.

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

210

Tukey J.W. (1992) The Future of Data Analysis. In: Kotz

S., Johnson N.L. (eds) Breakthroughs in Statistics.

Springer Series in Statistics (Perspectives in Statistics).

Springer, New York, NY.

Wolberg, W. H., Mangasarian, O. L., & Setiono, R. (1989,

January 1). Pattern recognition via Linear

Programming: Theory and application to medical

diagnosis. MINDS@UW Home. Retrieved October 1,

2021.

World Health Organization. (n.d.). Breast cancer. World

Health Organization. Retrieved November 6, 2021,

from https://www.who.int/news-room/fact-

sheets/detail/breast-cancer.

Predicting the Malignant Breast Cancer using Tumor Tissue Features

211