ANALYSIS OF THE UTILIZATION OF THE AUTOMATIC EXPOSURE CONTROL (AEC) FEATURE IN THE USE OF DEEP LEARNING BREAST IMAGE TECHNOLOGY IN WOMEN'S MAMMOGRAM SCREENING EXAMINATIONS AT DHARMAIS CANCER HOSPITAL
Mila Cahya Vidiani1, Leny Latifah2,
Yeti Kartikasari3
Politeknik
Kesehatan Kemenkes Semarang, Central Java, Indonesia
mila.vidiani@yahoo.com
|
KEYWORDS |
ABSTRACT |
|
Mammography,
Automatic exposure control (AEC), Deep learning |
Deep
learning technology is useful for radiology specialists as double reading to
help increase the accuracy of image interpretation results. One of the
preparations for maximizing the use of this technology is using good-quality
images as the source. The Automatic Exposure Control (AEC) feature, which
functions to determine exposure factors automatically, is expected to help
produce images with good and consistent quality so that deep learning
technology can work more effectively. This research aims to determine the
quality results of mammogram images taken using the AEC feature and to
analyze the use of deep learning technology in evaluating mammogram images.
This research method is retrospective by collecting 800 mammogram images
randomly and anonymously. Three hundred images were tested, 500 were
evaluated, and 250 were analyzed for image quality based on references
related to applying AEC and assessing the contrast-to-noise ratio (CNR). Deep
learning technology was analyzed by comparing the results of mammogram image
evaluation using deep learning and the evaluation results of a radiology
specialist. Deep learning technology analysis shows that 98% of mammograms
have the same results as the radiology doctor's evaluation, and 2% have
different results from the radiology doctor's evaluation where the image has
a dense breast type. The image quality results in this research showed that
97.6% of the 250 samples taken using the AEC feature had good image quality,
and 2.4% had poor image quality due to inappropriate breast positioning
during the examination. |
|
DOI: 10.58860/ijsh.v2i10.115 |
|
Corresponding Author: Mila Cahya Vidiani
Email: mila.vidiani@yahoo.com
INTRODUCTION
Cancer
is a disease characterized by several cells that grow uncontrollably and can
spread to other body parts (Wedayani & Hidajat, 2022). The World Health Organization (WHO) informs that
cancer is the second leading cause of death globally, and based on 2020
Globocan data, the type of cancer with the highest death rate in the world is
lung cancer. However, the highest number of new cases occurs in breast cancer (Despitasari & Dila, 2017). Not only in the world, but breast cancer is also cancer with the
highest number of new cases in Indonesia, where the number of cases reached
65,858 cases and the number of deaths reached 22,430 cases.
In several types of cancer, healing and survival can
be improved by making several efforts, including carrying out screening, early
detection, and increasing each individual's awareness of the risk signs and
early symptoms of cancer. Several risk factors that need to be known can
trigger breast cancer, namely age, family history, reproductive factors,
estrogen, and lifestyle (Suardita et al., 2016). The first
factor is related to age; women over 50 years of age are at greater risk of
developing breast cancer compared to women under 40 years of age (Sipayung
et al., 2022). The second factor is family
history. Women whose mothers or older sisters have had a history of breast
cancer will be more susceptible to this disease and have a 1.75 times higher
risk compared to women whose families have no history of breast cancer (Isnaini
& Elpiana, 2018). The third factor is reproductive
factors, such as women with late menopause and giving birth to their first
child at the age of 30 years and over (Wahyuningsih,
2018). The fourth factor is estrogen or
hormonal, such as using oral contraceptives or taking hormonal therapy for a
long time. The fifth factor is lifestyle, such as active or passive smoking,
alcohol consumption, eating too much fatty food, obesity, and lack of exercise,
which will increase the risk of developing breast cancer.
Regarding screening and early detection, the World Health Organization (WHO)
and the Ministry of Health of the Republic of Indonesia have recommended that
every woman carry out BSE (breast self-examination) and clinical examination by
a doctor (Gusti,
2018). If necessary, examination with a
mammography device can also be carried out as an initial step in screening and
early detection of breast cancer. Breast cancer screening with mammography aims
to detect breast cancer at an early stage that can hopefully be cured and to
anticipate continuous and linear tumor growth patterns (Abi
et al., 2019).
Mammography is a medical
examination method that uses low-dose X-rays to obtain images of breast tissue
that a radiology specialist can evaluate. This technique is included in non-invasive examinations, so the
examination is quite safe, painless, relatively cheap, and has reasonable
sensitivity (72-88%). Therefore, up to now, mammography is still the gold standard in supporting medical examinations
such as breast cancer screening (Fais,
2020).
The advantage of
mammography examination is that it can detect tumors or masses measuring less
than 1 cm that cannot be felt when palpated. However, evaluating masses on
mammogram images is very difficult because of the differences in shape, size,
and margins of different masses, especially in the breast. According to facts
in the field, mammography is a medical image that is difficult to evaluate or
interpret even by experts. Apart from images that are difficult to evaluate,
fatigue, experience, etc., from readers can cause differences in interpretation
(Suparmi
et al., 2023).
To reduce the risk of
errors in interpreting mammogram images and increase the rate of early
detection of breast cancer, several medical organizations, such as the American College of Radiology (ACR)
and the European Society of Radiology (ESR)
have created a program, namely the double
reading program. This program is a program that makes mammogram images
evaluated by two different radiologists independently. Several countries in
Europe and America have implemented this double
reading program, but in certain countries, especially Indonesia, following
and implementing this double reading
program is still very difficult due to the limited number of radiology
specialist doctors. The University of Indonesia Medicine informs that the
number of radiology specialist doctors in Indonesia currently (2021) is
approximately 1,646, serving more than 270 million people. This number still
needs to be increased to serve radiology services throughout Indonesia,
especially if you add the implementation of the double reading program (Utami & Handayani, 2017).
Therefore, until now, a
lot of science and technology continues to be developed to help radiology
specialists evaluate and interpret images consistently and accurately. One of
these technologies is deep learning,
part of artificial intelligence (AI) (Raup et al ., 2022).
Artificial intelligence (AI) is a branch of computer science that aims to build smart
devices to perform tasks requiring human intelligence. In its development, artificial intelligence (AI) uses
several methods, such as machine
learning and deep learning.
The deep learning method is a
sub-field of machine learning that
uses artificial neural networks, which consist of many layers to process data
in complex forms. In deep learning,
computers require large-scale data sets to increase the system's ability to
provide accurate output results. Currently, there are various programs and
technologies based on artificial intelligence (A.I.) that have been developed
to assist in the interpretation and analysis of mammogram images. One of the
A.I. mammography technologies is Transpara, an A.I. program from the
Netherlands that works with Dharmais Cancer Hospital, which is being used to
test A.I. accuracy on mammogram images.
This technology for radiology specialists can be used
as double reading in evaluating
images, especially mammograms. This can help radiology specialists increase the
accuracy of their interpretation results to reduce the possibility of false
negatives or positives (Agustina
et al., 2023). increase the confidence of radiology
specialists in the results of their interpretation, reduce reading time, and
reduce fatigue in interpreting mammogram images because the basis of mammogram
images is difficult to interpret.
Getting results from artificial intelligence
technology, in particular Deep
learning technology that meets expectations and can be relied upon in
medical practice, requires several things to be prepared, including a mammogram
image with good quality (not underexposed or overexposed). Poor or inadequate
imagery can affect the performance and accuracy of A.I. models (Marcheta,
2022).
Good and consistent mammogram image quality can be
obtained by utilizing the automatic
exposure control feature (AEC).
The AEC feature uses an algorithm that can help radiographers determine
kV, mAs, targets, and filters automatically based on breast thickness and image
needs, thereby producing images with good, optimal, and consistent quality.
Generally, image quality assessment
using the AEC feature is assessed using the contrast-to-noise ratio (CNR) descriptor.
Apart from getting good and consistent image quality,
the use of the automatic exposure
control feature (AEC) can
also help in reducing unnecessary radiation exposure to patients because
although medical imaging using X-rays provides enormous benefits in early
detection, there are still concerns about potential side effects such as
genetic diseases, cancer, etc. (Lingga,
2013). Patient dose assessment can be
determined from the average glandular
dose (MGD) as a patient dose descriptor.
Based on the results of
research titled Artificial Intelligence for Breast Cancer
Detection in Mammography: Experience of Use of the Screenprint Medical
Transparent System in 310 Japanese Women (2020), using a retrospective
study method on 310 patients, the results showed that A.I. performance lower
compared to human readers (Munawir
et al., 2023).
Meanwhile, according to research entitled Artificial Intelligence in Medical Imaging
of the Breast (2021), by identifying, segmenting, and classifying
lesions using AI breast imaging, the
results show that AI can reduce pressure on doctors, increase accuracy, and
optimize image sources (Sri,
n.d.).
Other researchers, in their research entitled
Automatic Exposure Control for a Slot Scanning Field Digital Mammography System
(2005), revealed that using AEC can reduce image repetition and improve
workflow (Ainiyah
et al., 2021).
This research has
theoretical benefits as a scientific source that can increase readers' insight,
especially students of the Department of Radiodiagnostic Engineering and
Radiotherapy, regarding the use of artificial intelligence technology. In
addition, the practical benefits of this research are expected to provide a
useful basis for the application of artificial intelligence in the evaluation
and interpretation of mammogram images as well as the use of Automatic Exposure
Control (AEC) in producing mammogram images that are of good, optimal and
consistent quality.
This study aims to evaluate the quality of mammogram
images in screening patients using the automatic exposure control (AEC) feature,
measure the average glandular dose (AGD) in breast screening examinations using
AEC, analyze the use of AEC as a standard procedure in carrying out breast
screening examinations and utilizing deep learning technology as double
reading, as well as measuring the accuracy of deep learning technology in the
use of AEC on women's mammogram screening images at Dharmais Cancer Hospital.
This research uses a
retrospective type of research by collecting data from mammogram images taken
some time ago, and the data source used is a secondary data source. The
population in this study is mammography screening images that have been taken
using the Automatic Exposure Control (AEC)
feature at Dharmais Cancer Hospital in the 2021 period, totaling 800 images;
300 images are used as test samples and have been taught on normal, benign and
malignant images as well as 500 images were used as samples to be evaluated by
the Transapara AI program and evaluated for image quality. Sampling will use a
systematic random sampling technique. By the recommendations of the American Cancer Society (ACS) and
the American College of Radiology (ACR),
samples will be taken from patients over 40 years. Images have been taken using
the AEC feature. The instrument in this research used an observation sheet—overall research and development
activities from March 2023 – June 2023.
RESULTS AND DISCUSSION
General description of the research site
A Brief History of Dharmais Cancer
Hospital
Dharmais Cancer Hospital was founded in May 1991, and
construction was completed on July 5, 1993. President HM Soeharto inaugurated
this hospital on October 30, 1993. He hoped that this hospital could
significantly contribute to efforts to prevent and cure cancer, accompanied by
a valuable research center and reliable medical service center that would
support cancer control programs. This is also by the duties and functions
stated in Minister of Health Decree No.72/Menkes/SK/I/1993 concerning the
organization and work procedures of RSKD, which has established installations
that operate in service, education, and research. (Kristianawati
et al., 2018) .
Then, in 2022, considering the vision that has been
set that in the future, RSKD will become a Cancer Hospital and National Cancer
Center which will become a role model in cancer control programs in Indonesia,
the RSKD Main Director Decree No HK.00.06.1.1812 concerning the formation of a
central preparatory committee RSKD national cancer is tasked with conducting an
assessment of the position, authority, functions and duties of the national
cancer center, then developing the concept of a cancer center and carrying out
cancer research, training and cancer registration (Usman,
nd).
In 2008, a review and redefinition of the role of the
Dharmais Cancer Hospital (RSKD) was carried out as part of the National Cancer
Center, where it was hoped that this institution could contribute to the
implementation of a real national cancer control program by supporting the
program launched by the Ministry of Health and finally the date November 1
2017, Dharmais Cancer Hospital Jakarta was designated as a national cancer
center based on R.I. No.HK.01.07/Menkes/531/2017 (Usman,
nd).
Hospital medical rehabilitation, education and
research installations, cancer burden data installations and networks, and all
other installations that support the smooth running of activities in the fields
of services, education, and research in the field of cancer (epidemiology,
clinical, molecular, clinical trials).
By the referral system currently in effect in
Indonesia, RSKD is a national referral. It is increasingly necessary to
evaluate and find the best work plan to treat patients at any stage or who have
experienced delays, whether diagnostic delays, treatment delays, or delays due
to the health management system. Educational collaboration and the return
referral system, as well as the synchronization of clinical practice guidelines
with community health centers and type C, D, and B hospitals, need to be
improved (Marwayani,
2021).
Vision and Mission of Dharmais Cancer
Hospital
As a Vertical UPT Hospital under the Directorate General of
Health Services, Ministry of Health, which specializes in treating cancer,
Dharmais Cancer Hospital plays a role in realizing strategic targets related to
increasing the availability and quality of referral health facilities. Apart
from that, Dharmais Cancer Hospital is also a National Cancer Center, which
must play an optimal role as a comprehensive cancer health service function, a
national cancer education and information center, and a national cancer data
and research center (Panigoro, 2014 ).
Types of services at Dharmais Cancer
Hospital
The types of services available at Dharmais Cancer Hospital
are medical and nursing services. Dharmais Cancer Hospital has superior
services such as an integrated laboratory, stem cell transplantation, minimally
invasive surgery, microsurgery, super microsurgery, lymphatic venous surgery,
ultra-radical surgery, cancer wound care, palliative, cancer pain management,
and complementary therapy. Apart from that, RSK Dharmais also has early cancer
detection services, systemic therapy installations, etc.
Early cancer detection services
The types of early cancer detection services provided
by Dharmais Cancer Hospital are:
a. Early detection of breast cancer:
Mammography ( age > 40 years), breast ultrasound (age < 40 years).
b. Early detection of cervical cancer:
Pap smear, HR-HPV, Quadrivalent HPV Vaccination.
c. Early detection of colon cancer:
Occult blood, M2PK, CEA.
d. Early detection of thyroid cancer:
thyroid ultrasound, lab, total T3, FT4, TSH.
e. Early detection of prostate cancer:
Prostate Ultrasound, PSA Lab, etc.
f. Early detection of liver cancer:
upper abdominal ultrasound, Lab, AFP, HBSAG, Anti HSB, Anti HCV.
g. Early detection of ovarian cancer:
Gynecological Ultrasound, Ca 125 Lab.
h. Early detection of lung cancer: MSCT
Scan Thorax, Ureum, Creatinine.
Descriptive analysis of the sample
The number
of samples used and evaluated in this study was 200 patients. It is known that
the lowest age in this study was 36 years, and the highest was 77 years, as
shown in Table 1.
Table 1 Number and age of the sample
|
Number of patients |
500 people |
|
Lowest patient age |
36 years old |
|
Highest patient age |
77 years old |
The minimum age for women at risk of breast cancer
recommended by the
American Cancer Society (ACS)
and the American College of Radiology (ACR)
to undergo breast screening examinations using mammography is a minimum age of
40 years. From the data in Table 2, it is known that two samples did not comply
with the recommendations or 0.4% of the total sample.
Table
2 Sample age
range
|
Age |
Amount |
Percentage |
|
<40 Years |
2 |
0.4% |
|
40 – 50 Years |
226 |
45.2% |
|
51 – 60 Years |
209 |
41.8% |
|
61 – 70 Years |
55 |
11% |
|
>70 Years |
8 |
1.6% |
|
Total |
500 |
100% |
Analysis of image
quality results using SdNR and PMMA thickness
The image quality of all mammogram samples totaling 200 images has been
evaluated by creating 3 Regions of
Interest (ROI), each ± 1 wide 
in the breast area and
1 ROI in the object-free area as in Figure 1, then the average ROI results.
Calculated using the Signal difference to Noise Ratio (SdNR) formula, equivalent to Contrast to noise ratio (CNR).
Figure 1 ROI of mammography image in RCC projection
After the Signal
difference to Noise Ratio (SdNR)
was evaluated by looking at or comparing it with the SdNR reference value in
mammography proposed by the IAEA, it was found that there were 5 (2.4%) mammography
images that were of poor quality and 195 (97.6%) mammography images that were
of poor quality. Good image, as shown in Table 3.
Table
3 Analysis of mammogram image quality results
|
Image quality |
Total image |
Percentage |
|
Not
good |
6 |
2.4% |
|
Good |
244 |
97.6% |
|
Total |
250 |
100% |
Suppose the image quality is seen
based on the thickness of the breast. In that case, it is known that poor image
quality occurs mostly in thin breasts in the amount of 5 (2%) images and 1
(0.4%) image in standard breasts.
Table
4 Cross-tabulation of breast thickness and image quality
|
Breast
girth |
Image quality |
Total |
|
|
Not good |
Good |
|
|
|
Thin
(<53mm) |
5 (2.0%) |
129 (51.6%) |
134(53.6%) |
|
Standard
(53-90mm) |
1 (0.4%) |
115 (46.0%) |
116(46.4%) |
|
Total |
5 (2.4%) |
195 (97.6%) |
250 |
Analysis of the mean
glandular dose of mammography screening patients
Information on the
average glandular dose in each mammography image can be seen in the DICOM tag
of each image or the text image, as shown in Figure 2 below.
Figure 2 Example of an RCC projection
mammogram image
One of the mammogram
image samples in the Right
Cranio-Caudal (RCC) projection above shows that the average dose is
1.0463 mGy.
The
results of the evaluation of 250 samples of female patients with mammography
screening showed that 250 or all patients in this study received an average
glandular dose of less than three mGy, as shown in Table 5.
Table
5 Analysis of the average patient glandular dose
|
Patient dosage |
Amount |
Percentage |
|
<3mGy |
250 |
100% |
|
≥3mGy |
-
|
0% |
This shows that each
patient received a radiation dose by the recommendations of the Indonesian
Nuclear Regulatory Agency, namely less than 3mGy.
Of the 250 samples that
have been evaluated, it is known that the average glandular dose is 0.9151 mGy,
with the lowest glandular dose being 0.31 mGy and the highest glandular dose
being 1.97 mGy, as shown in Table 6.
Table
6 Average patient glandular dose
|
Average
glandular dose of samples |
mGy |
|
Average (mean) |
0.9179 |
|
Minimum |
0.31 |
|
Maximum |
2.19 |
Cross-tabulation
analysis of deep learning technology results and radiology
specialist reading results
Table
7 Cross-tabulation analysis of A.I. results and doctor reading results
|
Results of deep learning (A.I.) technology |
Doctor's reading results |
Total |
||
|
Normal |
Benign |
malignant |
||
|
Tall (high risk of
malignancy) |
3 (0.6%) BI-RADS 1 |
2 (0.4%) BI-RADS 3 |
4 (0.8%) BI-RADS 5 |
9 |
|
Intermediate (benign) |
66 (13.2%) BI-RADS 2 |
29 (0.5%) BI-RADS 3 |
4 (0.8%) BI-RADS 4 |
99 |
|
Low (low/normal
risk) |
340 (68%) BI-RADS 1 |
49 (9.8%) BI-RADS 3 |
3 (0.6%) BI-RADS 4 |
392 |
|
Total |
409 |
80 |
11 |
500 |
Referring to Table 8 regarding the six categories of BI-RADS
(Breast et al.), which is an initial assessment and recommendation system used
to report mammography results, it is known that there are 343 images included
in BI-RADS category 1, 66 images included in category BI-RADS 2, 80 images
including BI-RADS 3, 7 images including BI-RADS 4, and 4 images including
BI-RADS 5 which in BI-RADS 4 and BI-RADS 5 require further action such as a
biopsy to confirm whether the lesion is those found to be malignant or benign.
A biopsy can help in identifying the type of breast cancer and determining an
appropriate treatment plan
Breast Imaging
reporting and data system ) categories
|
Category 0 |
Requires additional evaluation such as
follow-up examination or ultrasound, |
|
Category 1 |
Negative mammography: No abnormalities are
seen on mammography |
|
Category 2 |
Benign findings. An
obvious abnormality was found, but it is most likely not cancer or another
malignant abnormality. No further action is required. |
|
Category 3 |
Disorders that may be benign. An
abnormality is found that is likely benign but requires future monitoring
with mammography to ensure there are no changes. |
|
Category 4 |
Possibly malignant. A suspicious
abnormality was found, but it has not been determined to be malignant. Usually,
follow-up procedures such as a biopsy are carried out. |
|
Category 5 |
She is suspected of being malignant.
Abnormalities highly suspicious for cancer were found. A biopsy is usually
recommended for confirmation. |
|
Category 6 |
Confirmed cancer. |
Analysis of the use
of Automatic Exposure Control (AEC)
Information
on using the Automatic Exposure
Control (AEC) feature on an image can be seen in the DICOM tags of each image, as shown in
Figure 24.
Figure 3 Example of DICOM tags on one of the mammogram
images
The results of the evaluation of all samples or images show
that all 250 images in this study were taken using the Automatic Exposure Control (AEC) feature.
Table 9 Number of images using AEC
|
Automatic Exposure
Control |
Amount |
Percentage |
|
Using
AEC |
250 |
100% |
|
Do
not use AEC |
- |
- |
|
Total |
250 |
100% |
With the Mammomat inspiration type
mammography device used at the Dharmais Cancer Hospital, the parameters
automatically set by the system are kV and mAs, while the filter is selected
manually.
CONCLUSION
Based on research that
has been carried out on mammography images of breast screening patients at
Dharmais Cancer Hospital, the following conclusions can be drawn: The use of
the automatic exposure control (AEC) feature can help improve image quality
during mammography examinations. In this study, 97.6% of the images had good
image quality. The automatic exposure control (AEC) feature can provide an
optimal and safe patient radiation dose below 3mGy by recommendations from the
Nuclear Energy Regulatory Agency. In this study, the smallest average glandular
dose was 0.3mGy, and the highest was 2.19mGy. The automatic exposure control
(AEC) feature can be used as a standard protocol in breast screening
examinations because the AEC feature can set exposure parameters such as kV,
mA, and exposure time automatically based on the density of each patient's
breast tissue so that it can produce good image quality with optimal and safe
radiation dose.
The consistency of
mammogram image quality results produced using the automatic exposure control
(AEC) feature can help evaluate the use of artificial intelligence (A.I.)
because A.I. will be more effective in studying the patterns and
characteristics of lesions on mammograms. In this research, the accuracy of
using artificial intelligence technology with deep learning methods was 98%.
Currently, deep learning technology in evaluating dense breast images still has
difficulties or challenges because dense breast tissue has a complex structure,
making it difficult for A.I. to differentiate between dense and non-dense
tissue. This can affect the detection of lesions on mammogram images.
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