Breast CancerNCT06717984
AI Model for Classifying Breast Cancer From Histopathology Images
An observational study of Breast Cancer, run by Taufiq Hasan, PhD at 1 site in Bangladesh.
Trial summary
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. Read more about this study
- Who is it for?
- Any age, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Biopsy, Mastectomy, Histopathology
- How many people
- 500 participants
- Where
- 1 siteBangladesh
- Study dates
- 11 Jan 2024to 11 Feb 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The study population includes women who are undergoing breast biopsies or mastectomies due to suspected abnormal cell growth. Participants are drawn from hospitals and clinics where breast cancer screening and diagnostic procedures are conducted. This population includes individuals with a range of breast conditions, from benign abnormalities to noninvasive (in situ) and invasive breast cancers, representing various stages of breast disease for classification and diagnostic purposes.
Who may be able to join
- Female patients of any age 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Bangladesh1 site, 1 recruiting
- Recruiting
National Institute of Cancer Research & Hospital (NICRH)
Dhaka, 1212
Farida Arjuman, FCPS, MCPS, +8801715520165, drarju35cmc@gmail.com
About this study
Official title: A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. In response, this research seeks to revolutionize breast cancer diagnostics by using deep learning techniques to classify invasive and noninvasive breast cancer types from histopathological images. Non-invasive cancers, like DCIS and LCIS, are confined to milk ducts or lobules, while invasive cancers spread to surrounding tissue and make up 70% of cases, often leading to poorer outcomes.
The proposed AI model aims to enhance diagnostic accuracy and efficiency, surpassing manual methods, and providing a scalable solution for diverse healthcare settings. By automating image analysis, the model seeks to democratize cancer screening, making it accessible in underserved populations and adaptable to different resources and equipment. Ultimately, this research aims to advance breast cancer detection, improve patient care, and contribute to better treatment outcomes globally.
Read the detailed description
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.
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.
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, 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.
- Conditions
- Breast Cancer
- Being tested
- Biopsy, Mastectomy, Histopathology
- Keywords
- Breast Cancer, Histopathology, Deep Learning
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Biopsy, Mastectomy, HistopathologyDiagnostic test
Biopsy:
This intervention involves the collection of breast tissue samples through a biopsy procedure. These samples are obtained from women undergoing investigation for unusual cell growth and are analyzed to detect potential abnormalities, including benign, in situ, or invasive cancerous changes.
Mastectomy:
This intervention refers to the surgical removal of breast tissue, typically performed to treat or prevent the spread of breast cancer. It may involve the removal of part or all of the breast and is considered in cases of invasive breast cancer or high-risk noninvasive breast cancer.
Histopathology:
This intervention focuses on the microscopic examination of breast tissue samples collected through biopsy or mastectomy. Histopathological analysis is conducted to assess cellular abnormalities, determine the presence of cancer, and classify the tissue as benign, in situ, or invasive, providing the basis for diagnosis and treatment decisions.
| Study group | Role | What they receive |
|---|---|---|
| Women who undergo biopsy for suspected abnormal cell growth in the breast | The cohort includes women who have undergone a biopsy due to suspected abnormal cell growth in the breast. This cohort captures a wide range of potential diagnoses, including benign conditions, noninvasive (in situ) breast cancers, and invasive breast cancers. All participants have histopathological samples collected for analysis, which serve as the basis for determining the presence and type of abnormal cell growth. The cohort will be studied using deep learning techniques to classify the biopsy samples into specific categories (normal, benign, in situ, or invasive), with the goal of improving diagnostic accuracy and efficiency in detecting breast cancer.
By focusing on women undergoing biopsy, this study aims to address the diagnostic challenges faced in distinguishing between various breast tissue abnormalities, contributing to earlier detection and better clinical outcomes. Receives: Diagnostic Test: Biopsy, Mastectomy, Histopathology |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Deep Learning Model in Classifying Breast Tissue as Normal Benign, In Situ, or Invasive.
The primary outcome of this study is the diagnostic accuracy of the deep learning model in classifying histopathological images from breast biopsies into three categories: benign, in situ (noninvasive), and invasive breast cancer. Accuracy will be measured by comparing the model's predictions to the ground truth diagnoses determined by expert pathologists.
Measured: Measured at the time of histopathological image analysis (within 1 week of biopsy or mastectomy).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Dec 2024
- Study start11 Jan 2024
- Primary completion11 Feb 2025 (estimated)
- Study completion11 Feb 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Taufiq Hasan, PhD
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of Cancer Research & Hospital, Bangladesh
- Responsible party
- Sponsor-investigator, Taufiq Hasan, PhD, Bangladesh University of Engineering and Technology
- Investigators
- Taufiq Hasan, PhD, Principal investigator, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka - 1205.Farida Arjuman, FCPS, MCPS, Principal investigator, Department of Histopathology, National Institute of Cancer Research and Hospital (NICRH)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06717984
- Sponsor's protocol ID
- NICRH/IRB/2024/134
- Status last verified
- Dec 2024
- First submitted
- 1 Dec 2024
- Last updated
- 5 Dec 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 5 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on Artificial Intelligence Algorithms for Breast Cancer Classification From Histopathology Images
- ClinicalTrials.gov ID
- NCT06717984
- Acronym / sponsor ID
- NICRH/IRB/2024/134
- Conditions
- Breast Cancer
- Keywords
- Breast Cancer, Histopathology, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Taufiq Hasan, PhDUniversity, hospital or other
- Collaborators
- National Institute of Cancer Research & Hospital, Bangladesh
- Study start
- 11 Jan 2024 (actual)
- Primary completion
- 11 Feb 2025 (estimated)
- Study completion
- 11 Feb 2025 (estimated)
- First posted
- 5 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. In response, this research seeks to revolutionize breast cancer diagnostics by using deep learning techniques to classify invasive and noninvasive breast cancer types from histopathological images. Non-invasive cancers, like DCIS and LCIS, are confined to milk ducts or lobules, while invasive cancers spread to surrounding tissue and make up 70% of cases, often leading to poorer outcomes.
The proposed AI model aims to enhance diagnostic accuracy and efficiency, surpassing manual methods, and providing a scalable solution for diverse healthcare settings. By automating image analysis, the model seeks to democratize cancer screening, making it accessible in underserved populations and adaptable to different resources and equipment. Ultimately, this research aims to advance breast cancer detection, improve patient care, and contribute to better treatment outcomes globally.
Detailed description
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.
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.
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, 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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Women who undergo biopsy for suspected abnormal cell growth in the breast | – | Diagnostic Test: Biopsy, Mastectomy, HistopathologyThe cohort includes women who have undergone a biopsy due to suspected abnormal cell growth in the breast. This cohort captures a wide range of potential diagnoses, including benign conditions, noninvasive (in situ) breast cancers, and invasive breast cancers. All participants have histopathological samples collected for analysis, which serve as the basis for determining the presence and type of abnormal cell growth. The cohort will be studied using deep learning techniques to classify the biopsy samples into specific categories (normal, benign, in situ, or invasive), with the goal of improving diagnostic accuracy and efficiency in detecting breast cancer. By focusing on women undergoing biopsy, this study aims to address the diagnostic challenges faced in distinguishing between various breast tissue abnormalities, contributing to earlier detection and better clinical outcomes. |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population includes women who are undergoing breast biopsies or mastectomies due to suspected abnormal cell growth. Participants are drawn from hospitals and clinics where breast cancer screening and diagnostic procedures are conducted. This population includes individuals with a range of breast conditions, from benign abnormalities to noninvasive (in situ) and invasive breast cancers, representing various stages of breast disease for classification and diagnostic purposes.
Key inclusion criteria (5 listed)
Key exclusion criteria (4 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Biopsy, Mastectomy, HistopathologyBiopsy: This intervention involves the collection of breast tissue samples through a biopsy procedure. These samples are obtained from women undergoing investigation for unusual cell growth and are analyzed to detect potential abnormalities, including benign, in situ, or invasive cancerous changes. Mastectomy: This intervention refers to the surgical removal of breast tissue, typically performed to treat or prevent the spread of breast cancer. It may involve the removal of part or all of the breast and is considered in cases of invasive breast cancer or high-risk noninvasive breast cancer. Histopathology: This intervention focuses on the microscopic examination of breast tissue samples collected through biopsy or mastectomy. Histopathological analysis is conducted to assess cellular abnormalities, determine the presence of cancer, and classify the tissue as benign, in situ, or invasive, providing the basis for diagnosis and treatment decisions. | Diagnostic test | Women who undergo biopsy for suspected abnormal cell growth in the breast |
Primary outcome
Accuracy of Deep Learning Model in Classifying Breast Tissue as Normal Benign, In Situ, or Invasive.
Time frame: Measured at the time of histopathological image analysis (within 1 week of biopsy or mastectomy).
The primary outcome of this study is the diagnostic accuracy of the deep learning model in classifying histopathological images from breast biopsies into three categories: benign, in situ (noninvasive), and invasive breast cancer. Accuracy will be measured by comparing the model's predictions to the ground truth diagnoses determined by expert pathologists.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Dec 2024.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Taufiq Hasan, PhD principal investigator
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka - 1205.
Farida Arjuman, FCPS, MCPS principal investigator
Department of Histopathology, National Institute of Cancer Research and Hospital (NICRH)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Taufiq Hasan, PhD at 1 site in Bangladesh.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Bangladesh. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Taufiq Hasan, PhD (+8801817579844, taufiq@bme.buet.ac.bd), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Deep Learning Model in Classifying Breast Tissue as Normal Benign, In Situ, or Invasive. (time frame: Measured at the time of histopathological image analysis (within 1 week of biopsy or mastectomy).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Dec 2024.