A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
Conditions: Breast Cancer
Sponsor: Taufiq Hasan, PhD

Research Protocol:
A Study on Artificial Intelligence
Algorithms for Breast Cancer
Classification from Histopathology Images
Principal Investigator:
Dr. Taufiq Hasan
Associate Professor, Biomedical Engineering,
Bangladesh University of Engineering and Technology (BUET), Dhaka – 1205.
Co-Principal Investigator:
Dr. Farida Arjuman
MBBS(CMC), BCS(Health),
FCPS (Histopathology),MCPS(Clinical Pathology)
Associate Professor & Head
Department of Histopathology
National Institute of Cancer Research & Hospital (NICRH)
Mohakhali, Dhaka-1212
Date of submission:
18.03.2024

Application for Ethical Clearance
National Institute of Cancer Research and Hospital
Mohakhali, Dhaka-1212
1.
Principal Investigator(s):
Name: Dr. Taufiq Hasan
Qualification: B.Sc, M.Sc, PhD (UT Dallas, TX, USA)
Detailed address: Department of Biomedical Engineering, Bangladesh
University of Engineering and Technology (BUET), Dhaka-1205.
Mobile: 01817579844
Telephone (Off./Res.): N/A
e-mail: taufiq@bme.buet.ac.bd
2.
Co-Principal Investigator(s):
Name: Dr. Farida Arjuman
Qualification: MBBS(CMC), BCS(Health), FCPS(Histopathology),
MCPS(Clinical Pathology)
Detailed address: Department of Histopathology, National Institute of Cancer
Research & Hospital (NICRH), Mohakhali, Dhaka-1212
Mobile: 01715520165
Telephone (Off./Res.): N/A
e-mail: drarju35cmc@gmail.com
3.
Place of Study/Institution(s):
i)
National Institute of Cancer Research & Hospital (NICRH),
Mohakhali, Dhaka-1212
4.
Title of Study: A Study on Artificial Intelligence Algorithms for Breast
Cancer Classification from Histopathology Images
5.
Type of Study: Prospective and retrospective case control study
6.
Duration of study: 1 year
7.
Total cost: N/A
8.
Funding Agency: N/A
I agree to obtain approval of the NICRH Ethical Review Committee for any changes
involving the rights and welfare of subjects or any changes of the Methodology before
making any such changes.
Dr. Taufiq Hasan
Associate Professor, Department of Biomedical Engineering,
Bangladesh University of Engineering and Technology (BUET), Dhaka – 1205.
1

Dr. Farida Arjuman, MBBS(CMC), BCS(Health), FCPS (Histopathology),MCPS(Clinical
Pathology)
Associate Professor & Head, Department of Histopathology
National Institute of Cancer Research & Hospital (NICRH), Mohakhali, Dhaka-1212
2

Circle the appropriate answer to each of the following
(If not Applicable write N/A)
1.
Source of Population:
a.
ILL PArticipant
Yes
No
b. Non ILL Participant
Yes
No
c.
Minors or persons
Yes
No
under guardianship
2.
Does the study involve?
a.
Physical risks to
Yes
No
the subjects
b. Social Risks
Yes
No
c.
Psychological risks
Yes
No
to subjects
d. Discomfort to
Yes
No
subjects
e.
Invasion of the body
Yes
No
f.
Invasion of privacy
Yes
No
g. Disclosure of
Yes
No
information damaging to
subject or others
3.
Does the study involve?
a.
Use of records
Yes
No
(Hospital, medical,
Death, birth or other)
b. Use of foetal tissue
Yes
No
or abortus
c.
Use of organs or
Yes
No
body fluids
4. Are
subjects
clearly
informed
about?
a.
Nature and
Yes
No
purposes of study
b. Procedures to be
N/A
followed including
alternatives used
c.
Physical risks
N/A
d. Private questions
Yes
No
e.
Invasion of the body
N/A
f.
Benefits to be
Yes
No
derived
g. Right to refuse
Yes
No
to participate or
to withdraw from study
h. Confidential handling
Yes
No
of data
i.
Compensation
Yes
No
where there are risks
or loss of working time or
privacy is involved in
any particular procedure
5. Will
signed
consent
form/verbal
consent be required?
a.
From subjects
Yes
No
b. From parent or
Yes
No
guardian (if subjects
are minors)
6. Will precautions be
Yes
No
taken to protect
anonymity of subject?
3

RESEARCH PROTOCOL
●
Project Title: A Study on Artificial Intelligence Algorithms for Breast Cancer
Classification from Histopathology Images
●
Summary: Breast cancer, a widespread and potentially fatal illness, emphasizes the
urgent requirement for early and precise detection to enhance patient outcomes. The
current
diagnostic
framework,
characterized
by
manual
histopathological
examination, exhibits inherent drawbacks such as subjectivity and limited throughput.
In response, our research aims to transform breast cancer diagnostics by leveraging
advanced computational techniques, particularly deep learning. Breast cancer can be
classified into two main subtypes: invasive and noninvasive.
Noninvasive breast cancer, often termed in situ breast cancer, is characterized by
abnormal cells confined within the milk ducts (ductal carcinoma in situ or DCIS) or
lobules (lobular carcinoma in situ or LCIS) without invading surrounding tissues.
This type is considered an early stage and typically not life-threatening on its own.
Invasive breast cancers are those that spread from the original site (either the milk
ducts
or
the
lobules)
into
the
surrounding
breast
tissue.
These
constitute
approximately 70% of all breast cancer cases and generally have a poorer prognosis
compared to the in-situ subtypes. [1]
Medical imaging of breasts can be acquired through various techniques, such as MRI
scans, mammography, ultrasound, thermography, computed tomography scans, and
histopathology. Among these approaches, the histopathology test serves as the gold
standard for the clinical diagnosis of cancer. [2]
The
proposed
AI
model
aims
to
streamline
and
enhance
the
analysis
of
histopathological images for the classification of invasive and noninvasive breast
cancer,
surpassing conventional methods and providing a reliable means of
identifying cancerous regions. This promises to significantly improve the accuracy
and efficiency of breast cancer diagnostics, meeting the urgent need for dependable
and scalable solutions. By automating the complex process of breast cancer
histopathological image analysis, our goal is to democratize screening, making it
more accessible and reaching underserved populations. Moreover, our model goes
beyond technological innovation; it addresses broader issues of accessibility and
scalability, particularly in low-income settings. The research's focus on domain
adaptation is crucial, ensuring the model's accuracy and reliability across various
health facilities. This involves accommodating differences in resources, equipment,
and demographic factors, making it a versatile and adaptable solution for diverse
contexts.
In summary, our research not only aims to advance the technological frontier in breast
cancer diagnostics but also seeks to drive a transformative change in accessibility,
4

efficiency, and reliability. By developing an AI model that tackles the specific
challenges of traditional methods, we aim to make a meaningful impact on breast
cancer screening, ultimately leading to early detection, tailored treatment, and
improved outcomes for patients worldwide.
●
Introduction: Breast cancer is a condition where abnormal breast cells proliferate
uncontrollably, leading to the formation of tumors. If left untreated, these tumors can
metastasize, spreading to other parts of the body and potentially becoming fatal.
Breast cancer originates within the milk ducts or lobules of the breast. The initial
form, known as in situ, is non-life-threatening. However, cancer cells can infiltrate
nearby breast tissue, resulting in the development of tumors that manifest as lumps or
thickening. Invasive breast cancers have the potential to metastasize to nearby lymph
nodes or other organs, posing a significant risk of fatality.
Breast cancer comprises 12.5% of all new cancer cases annually worldwide, rendering
it the most prevalent cancer globally. In 2020 alone, 2.3 million women received a
breast cancer diagnosis, with approximately 685,000 succumbing to the disease,
solidifying its status as the most common cancer. By the close of 2020, 7.8 million
women had survived a breast cancer diagnosis within the previous five years,
establishing it as the world's foremost prevalent cancer. Breast cancer afflicts women
in every nation post-puberty, with incidence rates escalating in later stages of life. [3]
Breast cancer detection typically involves palpation, followed by non-invasive
mammogram-based
identification
of
critical
points.
For
confirmation
and
comprehensive disease profiling, invasive microscopic examination of biopsy samples
extracted from the breast's critical mass is conducted. [4]
Early and precise detection of breast cancer is paramount in reducing mortality rates,
significantly improving the prognosis and elevating patient survival rates up to 50%.
Treatment approaches vary depending on individual factors, cancer type, and extent of
spread, often combining surgical intervention, radiation therapy, and medication. In
histopathology, the standard procedure for diagnosing breast cancer involves
examining a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope.
However, this method relies on manual, qualitative inspection, leading to potential
investigator bias and low throughput. [5]
Manual examination of histopathological images is labor-intensive and taxing.
Moreover, there's a significant risk of overlooking small cancerous regions on slides,
especially as malignant cells may be sparsely distributed. In this scenario, machine
learning-based
automated
inspection
techniques
offer
rapid
and
dependable
identification of invasive and noninvasive cancerous cells and regions on the slide. [5]
Diagnoses heavily depend on the expertise of medical professionals, introducing
subjectivity and limited repeatability, with documented inaccuracies even among
experienced practitioners.
5

Traditional vision screening of histopathological images via light microscopy poses
risks of misdiagnosis and struggles with accessibility due to time and personnel
limitations. The primary focus is on leveraging advanced computational techniques to
automate analysis, addressing inherent drawbacks in traditional screening methods.
Through AI approaches, our aim is to create a model surpassing the accuracy of visual
assessments, providing a reliable and efficient means of identifying transformation
zones and detecting early signs of breast abnormalities. Central to our objective is the
recognition of the need for scalable and cost-effective solutions, particularly in
low-income settings. The proposed AI model seeks to mitigate the shortage of trained
personnel by automating breast cancer histopathological image analysis, streamlining
screening and enhancing accessibility. Moreover, we aim to incorporate domain
adaptation techniques to ensure the model's precision and resilience across diverse
health facilities, considering challenges such as resource constraints, equipment
variations, and demographic diversity.
●
Objectives: Our primary goal is to compile a diverse dataset of breast tissue
histopathology images, incorporating samples from various sources to ensure
representation across patient demographics and imaging equipment. Using this
dataset, we aim to develop a robust AI model for automated classification of invasive
and noninvasive breast cancer within histopathology images. Specific objectives
include:
a) Clinical Validation: Evaluate the AI model's performance through comprehensive
clinical validation, measuring sensitivity, specificity, and accuracy in classifying
invasive and noninvasive breast cancer types in histopathology images.
b) Usability Assessment: Assess the model's usability by involving healthcare
professionals in the evaluation process, measuring integration into clinical workflows,
result interpretability, and time efficiency during histopathology examinations.
c) Impact on Early Detection: Investigate the AI model's impact on early breast cancer
detection, analyzing its contribution to identifying cancer types at earlier stages for
potentially improved patient outcomes.
d) Generalizability: Evaluate the model's generalizability across diverse datasets,
including local sources, assessing its performance in different regions and healthcare
settings for broader clinical applicability.
e) Impact on Patient Outcomes: Examine the AI model's impact on patient outcomes,
such as reduced diagnosis time, increased accuracy in cancer type identification, and
enhanced treatment planning, considering potential benefits to patient well-being and
satisfaction.
f) Clinical Decision Support: Evaluate the model's effectiveness as a clinical decision
support tool, measuring its contribution to accurate diagnoses and treatment plans
during histopathology examinations.
6

Through these objectives, our research aims to advance breast cancer diagnosis,
providing a comprehensive solution that integrates seamlessly with clinical practices
and positively influences patient outcomes.
●
Rationale:
Histopathology
images
contain
valuable
information about tissue
structure, but manual interpretation can be subjective and time-intensive. Deep
learning offers a promising solution by automatically learning complex patterns,
potentially improving the accuracy and efficiency of breast cancer classification. This
research aims to leverage deep neural networks to analyze extensive datasets of
histopathology images, distinguishing between invasive and noninvasive breast
cancer with high accuracy. Automating this classification process aims to reduce
human error, expedite diagnoses, and support personalized treatment approaches.
Ultimately, the goal is to refine breast cancer diagnosis, enabling earlier intervention
and better patient outcomes.
●
Methodology: In the clinic-based approach for histopathological breast cancer
screening data collection, patient information will be directly gathered within
healthcare facilities to develop an AI algorithm for automated cancer detection. This
involves capturing essential metadata and clinical details, including histopathological
breast images. Utilizing clinic resources, the collected data will train the AI algorithm,
offering insights into abnormal findings likelihood during screening. Notably, privacy
is prioritized, excluding personally identifiable information in the data collection
process.
a) Study Population: This investigation comprises a prospective and retrospective
case-control study designed to develop and assess an AI-based diagnostic tool for
breast cancer detection through visual examination. The patient cohorts will include
confirmed breast cancer cases diagnosed via gold standard tests and individuals with
breast abnormalities resembling cancer (such as Adenosis, Duct Ectasia, Ductal or
Lobular Hyperplasia, Fibroadenomas, Fibrosis, Mastitis, Simple Cysts, or other
precancerous
lesions),
along
with
corresponding
confirmatory
test
results.
Additionally, data from individuals with suspected breast cancer (test status unknown)
or normal breast condition will be included to support unsupervised learning. The
primary aim is to assess the algorithm's ability to differentiate between these patient
categories through analysis of their medical image data.
▪Inclusion criteria:
●
Female patients of age 18 years or older can be selected as subjects.
●
Individuals willing to participate in breast cancer screening.
●
Availability for biopsy examination.
●
Women with no current or prior diagnosis of breast cancer.
●
Availability of relevant medical records for confirmation and comparison
purposes.
7

▪Exclusion criteria:
●
Pregnant women are excluded due to potential impacts on screening results and the
necessity for special considerations during pregnancy.
●
Individuals with severe medical conditions or circumstances that may render
histopathologic examination inappropriate or unsafe are excluded.
●
Patients with conditions that could interfere with the accuracy of screening results are
excluded.
●
Follow-up screenings are not included in this study.
b) Sample size calculation: Following the methodology of sample size calculation to
evaluate diagnostic performance (for algorithms in this case), we use the following
method of calculation for a target sensitivity and specificity incorporating disease
prevalence [7]:
𝑁𝑠𝑒𝑛≥𝑍
2(1−𝛼)/2  × 𝑠𝑒𝑛 (1 − 𝑠𝑒𝑛) 
𝑐
2× 𝑝
𝑁𝑠p𝑒c ≥𝑍
2(1−𝛼)/2  × 𝑠𝑝𝑒𝑐 (1 − 𝑠𝑝𝑒𝑐 )
𝑐
2×(1− 𝑝)
𝑁𝑠𝑒𝑛
=
Required sample size for sensitivity, 𝑠𝑒𝑛
𝑁𝑠𝑝𝑒𝑐
=
Required sample size for specificity, 𝑠𝑝𝑒𝑐
𝛼
=
Confidence level
(1−𝛼)/2 =
The Z-score for the confidence level 𝛼
𝑍
2
p
=
Prevalence of breast cancer
c
=
Confidence interval
sen
=
Estimated sensitivity
spec
=
Estimated specificity
●
The final sample size 𝑁is calculated based on the larger of the two values
𝑁𝑠𝑒𝑛and 𝑁𝑠𝑝𝑒𝑐. In this study, as we are using deep learning techniques, we
need the medical image samples to reflect the various cancerous,
precancerous and normal cases. For our analysis, we assume an 80%
confidence level (𝛼= 0.80) and prevalence of breast cancer for
self-reported instances, 𝑝= 0.261 [23]. A confidence interval of 5% is
selected (𝑐= 0.05). We estimate the sensitivity and specificity of our
algorithms to be at 75% (𝑠𝑒𝑛= 0.75 and 𝑠𝑝𝑒𝑐= 0.75). Using these values,
we obtain the sample size values: 𝑁𝑠𝑒𝑛= 471
and 𝑁𝑠𝑝𝑒𝑐= 166. Since
𝑁𝑠𝑒𝑛> 𝑁𝑠𝑝𝑒𝑐, the final sample size for the study will be:
8

𝑁= 𝑁𝑠𝑒𝑛= 471
We can set a target of N = 500.
c) Procedures: During the prospective phase of the study, data collection
will occur in clinical settings. Eligible patients will be approached during
clinic visits and presented with a consent form for their review. Upon
agreement, each participant will be assigned a unique identifier to
maintain data integrity and ensure patient anonymity. Subsequently,
patients
will
provide
essential
clinical
information.
Healthcare
professionals will conduct histopathological examinations, and specialized
equipment available in the clinic will be used to capture images of the
breast region. This method guarantees the collection of non-identifiable
data, unique data identifiers, and informed consent directly within the
clinical setting, meeting the specific requirements of histopathological
breast cancer screening.
In the retrospective phase of the study, additional data will be retrieved
from the clinic's database of prior screenings. Due to the retrospective
nature, obtaining consent from individual patients is impractical. To
address privacy concerns, any personally identifiable patient information
will be systematically excluded from the collected data. This ensures
compliance with privacy standards while leveraging valuable information
from past screenings. While informed consent cannot be obtained for this
dataset, the study will strictly adhere to ethical guidelines and prioritize
patient privacy during the data extraction process from the clinic's
database.
d)Methods of Data Collection:
●
Patient ID (unique identification number)
●
Date & Time of data input
●
Age
●
Histopathological breast image data
●
Region of Interest (ROI) Annotations:
▪Lesion size
▪Provisional diagnosis
▪Suggested management (e.g., routine screening, biopsy, etc.)
▪Patient’s relevant medical/surgical history
●
Additional Information (if available):
●
Relevant diagnostic test results
●
Reproductive history
The
hematoxylin and eosin (H&E) stained images obtained from
histopathological
assessment,
along
with
relevant
metadata,
will
significantly contribute to algorithm design and image analysis. These
9

images' standard test status and corresponding results will serve as the
ground truth for our detection algorithm.
e) Data Interpretation: Following the completion of data collection,
exploratory data analysis will be conducted to assess patient distribution
across
various
parameters,
including
transformation
zone
type,
histopathology, and relevant clinical factors. Subsequently, a balanced
dataset will be curated to ensure fair representation of image samples.
This curated dataset will serve as the foundation for training and
evaluating
the
artificial
intelligence
(AI)
algorithm
developed
for
automatic classification and detection of abnormalities in histopathological
breast screening cases. The trained model will generate predictions
regarding the presence or absence of abnormalities in the breast images.
To validate the accuracy and clinical relevance of the algorithm's
predictions,
the
results
will
undergo
verification
and
analysis
in
collaboration
with
expert
histopathologists.
Their
insights
will be
invaluable in refining the automatic algorithm, thereby improving its
precision in classifying and detecting potential abnormalities during
histopathological breast screenings.
f) Statistical Analysis: Once the dataset becomes available, statistical
analysis will center on evaluating the performance of the developed
artificial intelligence algorithms for automatic classification and detection
of abnormalities in histopathological breast screening cases. Key metrics
such as sensitivity, specificity, precision, recall, F-1 score, and overall
accuracy will be utilized for rigorous evaluation. To ensure robustness,
experiments involving n-fold cross-validation will be performed, with
mean values and standard deviations of performance metrics calculated.
This analysis will conclude with a comparative assessment of various deep
learning
models,
assessing
their
effectiveness
in
the
automatic
classification task.
Utilization
of
Results:
The study results will be pivotal in the
development and assessment of an automatic detection algorithm for
breast cancer screening, with the following primary objectives:
a) Automatic Detection: Evaluate and detect potential abnormalities in
histopathological images, furnishing an automated screening tool for
efficient identification of breast cancer risk.
b) Clinical Decision Support: Aid healthcare professionals by furnishing
an automated analysis to assist in prioritizing treatment strategies and
making informed clinical decisions based on the algorithm's findings.
10

c) Systematic Data Storage: Enable systematic storage of histopathological
images and pertinent metadata to bolster ongoing research efforts, all
while safeguarding individuals' privacy during the automated detection
process.
These outcomes are poised to significantly enhance the efficiency and
accuracy
of
breast
cancer screening, offering valuable support to
healthcare professionals in Bangladesh.
●
Facilities: In the case of collecting data from histopathological breast cancer
screening, no additional facilities are required except those the clinics are already
equipped with (e.g. image acquisition and data storage capacity).
●
Approval / Forwarding of the Head of Department / Institute / IRB. A forwarding
letter from the Department of Biomedical Engineering, Bangladesh University of
Engineering and Technology (BUET) is attached.
●
Flow Chart: The data collection activity and the corresponding timeline is described
in the Gantt chart below:
No.
Work/Activities
Months
1
2
3
4
5
6
1
Initial algorithm with publicly available
data
2
Collection of images and metadata
3
Homogenization and organisation of data
4
Data processing and analysis
●
Ethical Implications: The proposed retrospective/prospective case-control study
based on medical records is designed to develop and evaluate the diagnostic
performance of Artificial Intelligence (AI) algorithms that will detect breast cancer
stage from histopathological images. The study poses minimal risk to the subjects
according to the HHS (USA) definition.
●
References:
1. Cruz-Roa, Á., Gilmore, H., Basavanhally, A., Feldman, M. D., Ganesan, S.,
Shih, N., Tomaszewski, J. E., González, F. A., & Madabhushi, A. (2017).
Accurate and reproducible invasive breast cancer detection in whole-slide
images: A Deep Learning approach for quantifying tumor extent. Scientific
Reports, 7(1). https://doi.org/10.1038/srep46450
11

2. Toma, T., Biswas, S., Miah, M. S., Alibakhshikenari, M., Virdee, B. S.,
Fernando, S., Rahman, H., Ali, S. M., Arpanaei, F., Hossain, M. A., Rahman,
M. M., Niu, M., Parchin, N. O., & Livreri, P. (2023). Breast cancer detection
based on simplified deep learning technique with histopathological image
using
BREAKHIS
database.
Radio
Science,
58(11).
https://doi.org/10.1029/2023rs007761
3. World Health Organization: WHO & World Health Organization: WHO.
(2023,
July
12).
Breast
cancer.
https://www.who.int/news-room/fact-sheets/detail/breast-cancer
4. Majeed, H., Kandel, M. E., Han, K., Luo, Z., Macias, V., Tangella, K., Balla,
A., & Popescu, G. (2015). Breast cancer diagnosis using spatial light
interference microscopy. Journal of Biomedical Optics, 20(11), 111210.
https://doi.org/10.1117/1.jbo.20.11.111210
5. Das, K., Conjeti, S., Chatterjee, J., & Sheet, D. (2020). Detection of breast
cancer from whole slide histopathological images using deep multiple instance
CNN.
IEEE
Access,
8,
213502–213511.
https://doi.org/10.1109/access.2020.3040106
6. LabMedica International. (2015, September 16). Breast cancer diagnosis uses
spatial
light
interference
microscopy.
mobile.Labmedica.com.
https://mobile.labmedica.com/?option=com_mobile_article&Itemid=2947604
36
7. Hoque, M. R., Haque, E., & Karim, M. R. (2020). Cervical cancer in
low‐income countries: A Bangladeshi perspective. International Journal of
Gynecology & Obstetrics, 152(1), 19–25. https://doi.org/10.1002/ijgo.13400
12

INFORMED CONSENT FORM (PROSPECTIVE STUDY)
Title: A Study on Artificial Intelligence Algorithms for Breast Cancer Classification from
Histopathology Images
Principal Investigator:
Dr. Taufiq Hasan, Associate Professor,
Department of Biomedical Engineering, BUET, Dhaka – 1205
Please check all that are applicable:
☐I am an adult participant in this study.
☐I am the parent or guardian granting permission for a child in this study (the use of
"you" refers to "your child" or “your ward.”)
Are you participating in any other research studies? (Yes/No) _____
PURPOSE OF PROJECT
You are invited to participate in a project entitled Clinical Evaluation of Artificial
Intelligence Algorithms for Breast Cancer Detection from Histopathology Images. This
project focuses on developing an algorithm that will automatically detect invasive and
non-invasive cancer
types using histopathological images.The period of the study will be
between February 10, 2024 to August 31, 2024. You are selected as a possible participant in
the study
If you decide to terminate your participation in this study, you should notify Dr. Taufiq Hasan
at +8801817579844.
PROCEDURES
The procedure for collecting histopathology slide images for a breast cancer detection typing
AI algorithm involves recruiting eligible female patients, obtaining informed consent,
conducting routine clinical visits with breast tissue biopsies, preparing histopathology slides,
capturing
digital
images, encrypting and anonymizing data, extracting retrospective
information, compiling a diverse dataset, and ensuring ongoing privacy and ethical oversight.
The goal is to develop an accurate AI algorithm while prioritising patient privacy and
adhering to ethical guidelines.
13

The data collected during these sessions will be put into one of the databases of the
Department of BME, BUET and will be used for future research.
No traditionally used identifying information about you, such as your name, address,
telephone number, or national ID number, will be put into the public database. Your privacy
is very important to us, and we will use safety measures to protect it. Despite all the safety
measures that we will use, we cannot guarantee that your identity will never become known
PARTICIPANT RESPONSIBILITIES
As a participant, your responsibilities include:
●
Adhere to instructions provided by healthcare professionals during clinic visits for
data collection.
●
Contact the data collection coordinator or research study staff promptly if you have
any questions or concerns.
●
If you are a healthcare professional, particularly a histopathologist, ensure accurate
maintenance of the image ID to prevent data mix-ups.
●
Notify the coordinator or research staff directly if you decide to withdraw from the
study on the clinic-based system, ensuring seamless communication with the study
staff.
WITHDRAWAL FROM STUDY
If you first agree to participate and then you change your mind, you are free to withdraw your
consent and discontinue your participation at any time.
If you decide to withdraw your consent to participate in this study, you can directly inform
Dr. Taufiq Hasan at +8801817579844 or notify the study staff.
If you withdraw from the study for any reason all the data acquired from you will be removed
from the databases.
YOU MUST NOTIFY ABOUT YOUR DISSENT WITHIN THE PERIOD OF THE
STUDY BEING CONDUCTED.
The Protocol Director may also withdraw you from the study without your consent for one or
more of the following reasons:
●
Failure to follow the instructions during data collection.
●
The Protocol Director decides that continuing your participation could be harmful to
you.
●
You need other treatments that are not allowed in the study.
●
The study is cancelled.
●
Unanticipated circumstances.
●
Other administrative reasons
14

POSSIBLE RISKS, DISCOMFORTS, AND INCONVENIENCES
There may be mild discomfort during the procedure and, in rare cases, minimal bleeding or
infection at the biopsy site. You should contact the Protocol Director or the study staff if you
have any questions.
POTENTIAL BENEFITS
We cannot and do not guarantee or promise that you will receive any benefits from this study.
The study is being conducted for humanitarian purposes, which envision a long-term goal
that will benefit patients suffering from or at the risk of having breast cancer.
PARTICIPANT’S RIGHTS
You should not feel obligated to agree to participate. Your questions should be answered
clearly and to your satisfaction. If you decide not to participate, notify the project coordinator
or the study staff.
CONFIDENTIALITY
The results of this research study may be presented at scientific or medical meetings or
published in scientific journals. Your identity and/or your personal health information will not
be disclosed except as authorized by you or as required by law.
CONTACT INFORMATION
Questions, Concerns, or Complaints: If you have any questions, concerns or complaints about
this research study, its procedures, risks and benefits, or alternative courses of treatment, you
should ask the data collection coordinator, Dr. Taufiq Hasan. You should also contact him at
any time if you feel you have been harmed by being a part of this study at +8801817579844.
May we contact you about future studies that may be of your interest? (Yes/No)
Checking on the box with a click means you agree to be in this study.
☐Agreed
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JUSTIFICATIONS FOR INFORMED CONSENT WAIVER
(RETROSPECTIVE STUDY)
The proposed research is a case-control study that only uses medical records of
histopathology slides of breast tissues. As the number of samples required is large (500
according to our sample size calculation) compared to the monthly recorded cases, it is not
possible to return to the patients for acquiring their consent in case of previously recorded
data. Considering the high potential impact of the study, we earnestly request waiver of
informed consent. The justifications for this request is provided below:
1. On the topic of informed consent waiver, the regulatory authority of the United States,
Department of Health and Human Services (HHS) mentions in 45 C.F.R. § 46.116(d):
“An IRB may approve a consent procedure which does not include, or which alters,
some or all of the elements of informed consent set forth in this section, or waive the
requirements to obtain informed consent provided the IRB finds and documents that:
(1) The research involves no more than minimal risk to the subjects;
(2) The waiver or alteration will not adversely affect the rights and welfare of the
subjects;
(3) The research could not practicably be carried out without the waiver or
alteration; …”
2. On the topic of case control studies, the Bangladesh Medical Research Council (BMRC)
“Ethical Guidelines for Conducting Research Studies Involving Human Subjects” mentions
on Page 45:
“However, if it entails only a review of medical records, informed consent may not be
required and indeed may very often not be feasible”.
In light of the abovementioned regulatory guidelines described by HHS (USA) and BMRC,
we want to highlight that our study fulfils all of the above conditions as explained below:
1. The proposed research involves no more than minimal risk to subjects. According to HHS
document 45 C.F.R. § 46.102(i):
“Minimal risk means that the probability and magnitude of harm or discomfort
anticipated in the research are not greater in and of themselves than those ordinarily
encountered in daily life or during the performance of routine physical or
psychological examinations or tests.”
We will use de-identified medical records for the study. This falls into the category of
minimal risk as described above.
2. The research could not be carried out practicably without the waiver or alteration. Training
artificial intelligence algorithms require a huge amount of data from a large number of
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patients due to the overfitting problem (further detailed in the sample size calculation section
below). It is not practically possible to collect signed or verbal informed consent from each
patient from each hospital. In addition, most of the patients hospitalized due to COVID-19
are in severe conditions and are not capable of providing consent.
3. The waiver or alteration will not adversely affect the rights and welfare of the subjects.
Only their digital medical images and clinical history data will be used for the research.
Personal identifying information (name, etc.) will be removed at the source.
With the explanations provided above, we hope that the ethical review committee will be
kind enough to approve our request to waive the informed consent requirement for this
case-control study using medical records. As we have mentioned above, the study is of
potentially high impact and unfortunately not feasible without the waiver.
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