Automatic Breast Density Measurement and Prognostic Methods of

Postoperative Tamoxifen Therapy for Breast Cancer

Hui Li

, Jiao Li

, Haojiang Li

, Shuchao Chen

, Lizhi Liu

and Hongbo Chen

Life and Environmental Sciences College, Guilin University of Electronic Technology, Guilin, China

Sun Yat-sen University Cancer Center, Guangzhou, China

Keywords: Breast Cancer, Mammographic Density, Deep Learning, Breast Cancer Prognosis.

Abstract: In order to explore a prognostic analysis method of postoperative tamoxifen treatment for breast cancer from

mammography, the squeeze-and-convolutional Neural Network (SE-CNN) method was used to investigate

the model of mammographic density automatic extraction from mammography and the prognostic effect of

mammographic density on tamoxifen treatment for breast cancer. The results show that the mammographic

density change rate of the subjects before and 15 months after surgery was extracted, and the mammographic

density change rate cut value was obtained by density map method, and the subjects were divided into groups.

The progression-free survival was HR: 2.654(95%CI,1.102-6.395), P =0.030. Patients with high

mammographic density change rate had a better prognosis, while those with low mammographic density

change rate had a worse prognosis. It is concluded that mammographic density change rate value can be a

potential prognostic factor of postoperative tamoxifen treatment for breast cancer.

1 INTRODUCTION

Breast cancer is the most common cancer in women

and the leading cause of cancer deaths in women

worldwide (Siegel 2020, Peng 2020, Deng 2020).

Endocrine therapy is an important part of the

comprehensive treatment of breast cancer (Oladeru

2020) and plays an important role in the prevention

of postoperative recurrence and metastasis of breast

cancer (Chlebowski 2021). Tamoxifen is an estrogen

receptor antagonist, which can combine with estrogen

receptors on the cell membrane to form a complex,

thus preventing estrogen from playing a role, and is

an important postoperative treatment for breast

cancer (Rahem 2020). It is of great clinical

significance to study the key prognostic factors of

postoperative tamoxifen treatment for breast cancer.

In recent years, some researchers have been

committed to studying the prognostic factors of

postoperative tamoxifen treatment for breast cancer

from the perspective of bioinformatics. Flap

endonuclease-1(Xu 2021), PDHA2-APRT gene pair

https://orcid.org/0000-0002-6110-6920

https://orcid.org/0000-0002-8980-2899

https://orcid.org/0000-0003-0784-1235

(Lv 2019), MFG-AS1(Feng 2020), etc., have been

considered as important prognostic biomarkers for

breast cancer after tamoxifen treatment. CXCL10 can

be used as a biomarker to predict the prognosis of

breast cancer and as a therapeutic target for tamoxifen

resistance (Wu 2020). The high expression of TRAF4

predicts a poor prognosis in the treatment of breast

cancer with tamoxifen, and promotes tamoxifen

resistance (Zhou 2020). In addition, there are some

studies looking for prognostic markers from medical

images. For example, surface dispersion coefficient in

magnetic resonance imaging has been proved to be a

marker that can be used to evaluate the effectiveness

of tamoxifen in the treatment of breast cancer (Zhai

2013).

Mammography is the most common examination

method for breast cancer patients, and it is of clinical

significance to obtain accurate prognostic markers

from mammography. Mammographic density (MD)

refers to the proportion of breast glandular tissue in the

overall projection of the breast in Mammographic

images (Sherratt 2016). It is a measure of the relative

quantity of glandular tissue in the breast (Bell 2020)

https://orcid.org/0000-0002-3579-4812

https://orcid.org/0000-0001-7875-9074

https://orcid.org/0000-0002-7063-5410

Li, H., Li, J., Li, H., Chen, S., Liu, L. and Chen, H.

Automatic Breast Density Measurement and Prognostic Methods of Postoperative Tamoxifen Therapy for Breast Cancer.

DOI: 10.5220/0011161300003444

In Proceedings of the 2nd Conference on Artiﬁcial Intelligence and Healthcare (CAIH 2021), pages 39-46

ISBN: 978-989-758-594-4

and has been proved to be related to the sensitivity and

specificity of breast cancer screening (Lynge 2019).

MD has been proposed as a biomarker to predict the

risk of breast cancer, the possibility of cancer

recurrence, the response to neoadjuvant chemotherapy

and survival rate (King 2011). Changes in MD reflect

changes in the amount of collagen and epithelial and

non-epithelial cells in mammary gland (Boyd 2011).

MD is not a static characteristic, and unlike most

breast cancer risk factors, MD can be changed (Boyd

2011), and the change of MD is associated with the

increased risk of breast cancer, advanced tumor stage

at diagnosis, local recurrence and the increased risk of

the second primary cancer (Huo 2014). Increased MD

is associated with increased risk of breast cancer, and

reduced MD is accompanied by reduced risk (Román

2019).

In clinical practice, MD is obtained primarily on

the basis of subjective visual assessments that rely on

radiologists, and has been shown to have significant

intra-physician and inter-physician variability.

Cumulus Software, a quantitative imaging analysis

software, has been developed for quantitative

measurement of dense tissue in breast by

molybdenum target (Byng 1998), which is the gold

standard for MD measurement (Nguycn 2018, Boyd

2010, Kerlikowske 2015). This is a semi-automatic

observer aid based on an interactive threshold. The

observer subjectively selects a threshold gray level

that facilitates recognition, separating glandular

tissue from fat. The interactive computer-aided

segmentation program based on the K-means

clustering algorithm measured MD, requiring manual

judgment of whether it was pectoral muscle, and then

segmental glandular tissue based on the K-means

clustering algorithm, and then calculated MD (Glide

2007). However, these semi-automatic MD

measurement methods require training of observers

and the measurement results are subject to subjective

factors of observers. A gland probability map is

generated in the method of MD estimation based on

Deep Convolutional Neural Network (DCNN), and

MD is estimated according to the ratio of the gland

probability map to the breast area (Li 2018).

Segmentation of breast and dense fibroglandular

region based on full convolutional network, this

method uses VGG16 network as the basic network

structure and fine-tuning network to achieve

segmentation of breast and gland dense region

respectively (Lee 2018). However, the MD

distribution of each patient was different, and the

mammary gland in some molybdenum target images

showed scattered distribution, dark gray scale and

fine structure. Deep learning probability maps or

segmentation methods can better segment the dense

areas of glands, but some non-densely aggregated

glandular tissues are often ignored, resulting in

deviations between measured MD and the actual

value.

Aiming at the existing problems in breast density

measurement, we proposed an automatic breast

density measurement method based on deep learning.

Firstly, the deep learning method is used to achieve

the precise segmentation of breast region. Then, the

Squeeze-and-Excitation Convolutional Neural

Network (SE-CNN) for MD is used to realize the

automatic measurement. To obtain accurate MD

value of breast cancer patients. In order to study the

key factors for the evaluation of postoperative

tamoxifen treatment for breast cancer, we analyzed

the prognostic capability of Mammographic density

change ratio (MDCR) before and after treatment to

explore the prognostic analysis method of

postoperative tamoxifen treatment for breast cancer.

This paper attempts to find breast cancer patients with

good postoperative tamoxifen treatment effect from

the perspective of imaging and improve the treatment

effect of breast cancer.

2 MATERIALS AND METHODSM

2.1 Dataset

This study was approved by the Ethics Committee of

Cancer Center of Sun Yat-sen University with the

approval number szR2020-170. The data were all

from the Cancer Prevention and Treatment Center of

Sun Yat-sen University, and there were two

independent data sets, model data and prognostic

data. The model data was used to train the MD

automatic measurement model, and the prognostic

data was used to obtain MDCR, and to analyze the

prognosis of postoperative tamoxifen treatment for

breast cancer.

2.1.1 Model Data

In the training of MD automatic measurement model,

due to the subjectivity and inaccuracy of manual

labeling threshold when setting threshold label in SE-

CNN threshold regression network model, Selenia

Dimensions instrument newly introduced by Cancer

Prevention and Treatment Center of Sun Yat-sen

University can indirectly obtain the gray threshold of

gland area. This label can avoid the error caused by

manual labeling. Therefore, data from the machine

were used to train the MD automatic measurement

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

model, including 246 molybdenum target images

from 246 patients, which were collected from March

2021 to June 2021, and the image resolution was

1136×944.

2.1.2 Prognostic Data

In this dataset, there were 858 mammographic images

with a resolution of 1915×2295. The data set was

from 429 patients. The patients were first treated from

April 2008 to December 2015. During follow-up, all

patients were treated with tamoxifen according to the

NCCN Breast Cancer Clinical Practice Guidelines

(8th edition) (Gradishar 2021). The time points of

molybdenum imaging were before surgery and 15

months after endocrine therapy for each patient, with

a median age of 44(25-61) years, a median height of

158(142-170) cm, and a median weight of 56(35-165)

kg. Median follow-up time was 59.8(6-82) months,

resulting in overall survival, progression-free

survival, distant metastasis-free survival, and

regional relapse-free survival.

2.2 Experiment

As shown in Figure 1, this experiment is divided into

two parts. The first part is the training and verification

of the MD automatic measurement model based on

model data, and the model with the best effect is

found through the five-fold cross-verification. Then,

based on the prognostic data, the MDCR value of the

prognostic data was obtained by the MD automatic

measurement model, and the prognostic ability of

MDCR value as a prognostic factor was obtained by

the prognostic analysis method.

Figure 1: Experimental design block diagram.

2.3 MD Automatic Measurement

Model

MD refers to the ratio of gland area to breast area, that

is, MD=GA/BA, where GA and BA represent gland

area and breast area respectively. As shown in Figure

2, the U-Net Plus method was firstly used to segment

the breast area and obtain the breast area BA. Then

the SE-CNN network was used to achieve the gray

threshold regression of the breast area and obtain the

threshold of gland segmentation in the breast area, so

as to realize the extraction of the gland area and

obtain the gland area GA. Finally, the MD value was

calculated.

Figure 2: Flow chart of MD automatic measurement model.

2.3.1 Breast Region Segmentation based on

U-NET Plus Network

U-net Plus network (Chen 2019) performs well in the

segmentation of esophageal cancer and esophageal

cancer in 2d CT slices. Its advantage lies in the use of

two U-shaped structures to enhance the extraction

ability of complex and abstract features, which can

effectively solve the problem of irregular and fuzzy

boundary segmentation. In the molybdenum target

Automatic Breast Density Measurement and Prognostic Methods of Postoperative Tamoxifen Therapy for Breast Cancer

image used in this paper, the boundary of the breast

area is dark and the contour is fuzzy. The U-Net Plus

network can effectively complete the segmentation

task of the breast area and remove the surrounding

muscle and nipple tissues.

After testing, the DICE value of breast region

segmentation based on U-NET Plus method reached

0.997, which accurately segmented the breast region.

This network can be used for accurate segmentation

of breast region in molybdenum target image.

2.3.2 Breast Threshold Extraction based on

SE-CNN Network

In molybdenum images, adipose tissue appears to be

grayscale smaller, while glandular tissue appears

opaque or grayscale larger. Therefore, glandular

tissue and adipose tissue can be distinguished

according to gray threshold. Since most of the

glandular tissues in the breast image are concentrated

and a few are scattered, and the glandular tissues have

different luminance, the realization of threshold

regression requires stronger feature extraction ability

of the network. Based on the channel attention

mechanism, the SE-CNN model is proposed to meet

this requirement. As shown in Figure 3, the27 model

consists of block modules of convolution layer,

nonlinear layer and maximum pooling layer. In order

to deepen the number of convolution layers, the

model uses five block modules to enhance the

network's feature extraction capability. At the same

time, the model studies the dependencies between the

channels. The attention is paid to the channels. The

SE(Squeeze-and-Excitation) module is added to each

block. Then according to this importance, the useful

features are promoted and the useless features are

suppressed to improve the accuracy of threshold

regression.

Figure 3: Diagram of SE-CNN network structure.

2.4 MDCR Calculation

The objective of this study is to explore the effect of

MDCR value on postoperative tamoxifen treatment

for breast cancer. The automatic measurement

method proposed in this paper calculates the MD

values before and after surgery respectively, so as to

obtain the change rate of MD, NAMELY,

MDCR=(MD1-MD2)/MD1, where in MD1

represents the breast density of the patient before

surgery, and MD2 represents the breast density of the

patient from endocrine therapy to follow-up time.

2.5 Statistical Methods

2.5.1 MD Automatic Measurement Model

Evaluation Method

In this paper, deep learning method is used to study

the MD automatic measurement model, so as to

realize the automatic calculation of MDCR value.

The breast area was extracted by U-NET Plus

method. In this paper, we propose a method to

determine the gland area by determining the threshold

value of gland extraction in molybdenum target

image, so as to obtain accurate MD value. We used

threshold absolute error (MAE), determination

coefficient (R^2) and Bland-Altman consistency

analysis to evaluate the performance of the new

threshold extraction method for glands. They are

defined as:

predict label

MAE

num

value value

−



(1)

()

predict label

predict

label

mean

value value

=−

−



−



(2)

2.5.2 Prognostic Evaluation Method

MDCR cutting value was solved by density map

method in R language, and patients were divided into

groups. K-M survival analysis was used to analyze

the effect of single factor on the prognosis of

postoperative tamoxifen treatment for breast cancer.

When P<0.05, the difference in progression-free

survival between the two groups was considered

statistically significant. Cox regression analysis,

using mathematical model to fit the relationship

between survival distribution and impact factors,

evaluate the impact of impact factors on the

distribution of survival function, further verify the

clinical significance of the prognostic method.

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

3 RESULTS

3.1 MD Automatic Measurement

Results and Analysis

In the threshold regression module, the results of

AlexNet, Vgg11 and ResNet50 models were

compared with se-CNN model. As shown in Table 1,

the average absolute error of threshold regression

results of SE-CNN network was lower than the other

three models, and the determination coefficient was

higher than the other three models. It indicates that

the threshold regression results of SE-CNN network

are more accurate.

Bland-altman consistency analysis was

performed for AlexNet, VGG11, ResNet50 and SE-

CNN model threshold output and machine threshold

Table 1: Comparison of experimental results of threshold

regression model.

MAE R^2

AlexNet 12.64±5.88 0.63

VGG11 12.03±6.96 0.67

ResNet50 10.94±5.29 0.70

SE-CNN 10.41±4.78 0.74

label. Figure 4A is bland-Altman diagram of SE-

CNN model. It can be seen from the figure4 that

98%(48/49) samples are within the 95% consistency

limit. Within the consistency limit, the maximum

absolute value of difference between two groups of

data is 24.43, and the average value of difference is

0.33. Therefore, compared with the other three

models, the threshold result of SE-CNN model has

the best consistency and is the most accurate and

reliable.

Figure 4: Bland-Altman consistency analysis of threshold regression results.

3.2 MDCR Cut Value and Subjects

Were Grouped

In order to analyze the prognosis of postoperative

tamoxifen treatment for breast cancer, breast cancer

patients need to be grouped. Nuclear density

estimates were calculated and plotted using a density

map method independent of patient prognostic

information. The density map is performed by R's

package GGplot2 and the function geom_density (a

smoothed version of the histogram). As shown in

Figure5A, when the MDCR value is 5.3, the curve

reaches its peak and can be used as the optimal cutting

value. To evaluate the validity of the cut values, the

RMS software package of R was used to perform the

restricted cubic spline (RCS) function, and the Cox

regression model of the overall survival rate was

established using the RCS formula parameters. As

shown in Figure5B, with the increase of MDCR, the

risk of breast cancer progression decreases, and the

critical value of risk reduction is reached when

MDCR=5.3, which further verifies the reliability of

cutting value selection. We used MDCR=5.3 as the

cut-off value to divide breast cancer patients into two

groups and analyze the prognosis of postoperative

tamoxifen treatment.

Automatic Breast Density Measurement and Prognostic Methods of Postoperative Tamoxifen Therapy for Breast Cancer

Figure 5: MDCR cutting value analysis.

3.3 Effect Evaluation of Tamoxifen

after Breast Cancer Operation

After obtaining MDCR cut values, breast cancer

patients were divided into two groups: Group

A(MDCR≥5.3) and Group B(MDC<5.3). Figure 6

shows the survival curve between the two groups.

The P-value of progression-free survival between the

two groups was 0.032 by Logrank test. There was a

significant difference between the two groups. It

can also be found that when MDCR≥5.3, the patient

survival curve is above, that is, the postoperative

tamoxifen treatment effect of this group of breast

cancer patients is better than that of the other group.

Multivariate Cox regression analysis was shown in

Table 2. Similarly, it was found that the risk rate of

MDCR was 2.654(95%CI,1.102-6.395), P=0.030.

These results suggest that MDCR can be used as a

key prognostic factor for postoperative tamoxifen

treatment of breast cancer.

Cox regression analysis was used to determine the

independent influencing factors of breast cancer

progression. Firstly, k-M analysis was performed to

screen out some meaningless variables, and factors

with p value less than 0.1 were included in Cox

regression model. K-M method was used to analyze

the following factors: age, height, weight, BMI and

MDCR, and the analysis results showed that age,

BMI and MDCR were correlated with the progression

of breast cancer. The results of Cox multivariate

analysis were shown in Table 2. MDCR (HR=2.654,

95%CI,1.102-6.395, P =0.030), BMI (HR=0.272,

95%CI, 0.088-0.846, P =0.024), Was an independent

risk factor for metastasis or recurrence in breast

cancer patients treated with tamoxifen. Follow-up

breast cancer patients with lower MDCR and higher

BMI had an increased risk of metastasis or

recurrence.

Figure 6: Survivorship curve.

4 CONCLUSIONS AND

DISCUSSIONS

In this paper, the following conclusions were drawn

through the study of automatic breast density

extraction model and the analysis of postoperative

tamoxifen treatment effect for breast cancer.

(1) In order to perform automatic prognostic

analysis of breast cancer, this paper proposed a

channel attention-based SE-CNN network to

accurately calculate breast density from molybdenum

target X-ray images. It can be seen from the

comparative analysis of performance that the

determination coefficient R^2=0.74, and from the

consistency analysis that 98% of samples are

distributed within an acceptable range. Therefore,

SE-CNN network can achieve accurate and automatic

extraction of MD.

(2) During prognostic analysis, MDCR cutting

value was obtained by density method, and the

subjects were divided into groups. The progression-

free survival between the two groups was HR

2.654(95%CI,1.102-6.395), P =0.030, showing a

significant effect. Patients with high MDCR had a

better prognosis, while those with low MDCR had a

worse prognosis. It indicates that MDCR value can be

used as a potential prognostic factor of postoperative

tamoxifen treatment for breast cancer, and can assist

doctors in finding patients with good prognosis after

postoperative tamoxifen treatment for breast cancer.

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

Table 2: Multivariate Cox multivariate analysis.

Progres

sion-

Free

Recurrence/

Metastasis

HR (95%) P value

Adge/(year) 2.119(95%CI,0.905-4.964) 0.084

<44(n=209) 192 17

≥44(n=220) 212 8

BMI 0.272(95%CI,0.088-0.846) 0.024

<22(n=184) 176 8

≥22(n=245) 228 17

MDCR 2.654(95%CI,1.102-6.395) 0.030

<5.3(n=220)

202 18

≥5.3(n=209) 202 7

The contribution of this paper is mainly in two

aspects: 1) It explores a method to automatically

obtain accurate MD value, which effectively solves

the impact of human intervention on the prognosis of

breast cancer. The introduction of channel attention

mechanism helps the network model to be more

sensitive to glandular tissue and achieve accurate,

complete and automatic extraction of glands.2) The

prognostic factors of breast cancer in mammography

were explored and verified. MDCR has good

prognostic performance in breast cancer and can be

used as a prognostic factor for postoperative

tamoxifen treatment of breast cancer.

ACKNOWLEDGEMENTS

On the completion of this paper, I would like to

express my heartfelt thanks and respect to Hongbo

Chen and the students in the research group. This

project has been applied for national Natural Science

Foundation of China (81760322, 82171906) and

university-level innovation Project 2021YCXS174.

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