Breast CancerNCT04156880
Artificial Intelligence in Mammography-Based Breast Cancer Screening
An observational study of Breast Cancer, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial summary
Breast cancer (BC) is the most common cancer among women in worldwide and the second leading cause of cancer-related death. As the corner stone of BC screening, mammography is recognized as one of useful imaging modalities to reduce BC mortality, by virtue of early detection of BC. 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?
- mammography
- How many people
- 0 participants
- Where
- 1 siteHong Kong
- Study dates
- 1 Jul 2020to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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: This is a single institutional retrospective cohort study of patients with mammographic examinations. All patients' data will be retrieved via the electronic patient database of our institution. Patient demographics, imaging and histological data, disease and treatment history will be recorded, including age at onset, details of chemotherapy, time interval of metastasis from diagnosis, surgery for the primary tumor, the length of survival, clinical outcome and so on.
Who may be able to join
- Women who had undergone standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views..
- Histopathology-proven diagnosis is available for patients with breast malignancy, including invasive breast cancer, carcinoma in situ, and borderline lesion et al.
- As reference standard of benign nature, results from pathology or clinical long-term follow-up (>=2 years) examinations are available for cases without breast malignancy.
Who cannot join
- Patients with concurring lesions on mammograms that may influence subsequent AI post-process.
- Patients without available pathologic diagnosis or long-term follow-up (>=2 years) examinations.
- Patients who had undergone breast surgical intervention (e.g. lumpectomy and mammoplasty) prior to first mammography.
- Patients diagnosed with other kinds of malignancy, concurrent with metastasis or infiltration/invasion to breast.
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin
About this study
Official title: Breast Cancer Screening With Mammography: Diagnostic Assessment of an Artificial
Breast cancer (BC) is the most common cancer among women in worldwide and the second leading cause of cancer-related death.
As the corner stone of BC screening, mammography is recognized as one of useful imaging modalities to reduce BC mortality, by virtue of early detection of BC. However, mammography interpretation is inherently subjective assessment, and prone to overdiagnosis.
In recent years, artificial intelligence (AI)-Computer Aided Diagnosis (CAD) systems, characterized by embedded deep-learning algorithms, have entered into the field of BC screening as an aid for radiologist, with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction. For now, stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis, largely attributed to utilization of convolution neural network(CNNs), and some of them have already achieved radiologist-like level. On the other hand, radiologists' performance on BC screening has shown to be enhanced, by leveraging AI-CAD system as decision support tool. As increasing implementation of commercial AI-CAD system, robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption, like other emerging and promising technologies. This requires to validate AI-CAD systems in BC screening on multiple, diverse and representative datasets and also to estimate the interface between reader and system. This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms. In this work, we will employ a commercially available AI-CAD tool based on deep-learning algorithms (IBM Watson Imaging AI Solution) to identify and characterize the suspicious breast lesions on mammograms. The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported. After AI post-processing, we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction.
- Conditions
- Breast Cancer
- Being tested
- mammography
- Keywords
- mammography, artificial intelligence, 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.
mammographyOther
standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views
What is being measured
The primary outcome is the main question the study is designed to answer.
area under curve (AUC)
area under receiver operating characteristic (ROC) curve in percentage (%)
Measured: 3 years
accuracy
proportion of true results(both true positives and true negatives) among whole instances
Measured: 3 years
sensitivity
true positive rate in percentage(%) derived by ROC analysis
Measured: 3 years
specificity
true negative rate in percentage (%) derived by ROC analysis
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Nov 2019
- Study start1 Jul 2020
- Primary completion31 Dec 2023
- Study completion31 Dec 2023
Why it stopped: the collaborating party of AI system withdraw their study due to change of company policy in year of COVID-19 pandemic
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.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- IBM China/Hong Kong Limited
- Responsible party
- Principal investigator, Professor Winnie W.C. Chu, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04156880
- Sponsor's protocol ID
- 2019.629
- Status last verified
- Feb 2024
- First submitted
- 6 Nov 2019
- Last updated
- 7 Feb 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Breast Cancer Screening With Mammography: Diagnostic Assessment of an Artificial
- ClinicalTrials.gov ID
- NCT04156880
- Acronym / sponsor ID
- 2019.629
- Conditions
- Breast Cancer
- Keywords
- mammography, artificial intelligence, deep learning
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: the collaborating party of AI system withdraw their study due to change of company policy in year of COVID-19 pandemic
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Collaborators
- IBM China/Hong Kong Limited
- Study start
- 1 Jul 2020 (actual)
- Primary completion
- 31 Dec 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 8 Nov 2019 (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 (BC) is the most common cancer among women in worldwide and the second leading cause of cancer-related death.
As the corner stone of BC screening, mammography is recognized as one of useful imaging modalities to reduce BC mortality, by virtue of early detection of BC. However, mammography interpretation is inherently subjective assessment, and prone to overdiagnosis.
In recent years, artificial intelligence (AI)-Computer Aided Diagnosis (CAD) systems, characterized by embedded deep-learning algorithms, have entered into the field of BC screening as an aid for radiologist, with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction. For now, stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis, largely attributed to utilization of convolution neural network(CNNs), and some of them have already achieved radiologist-like level. On the other hand, radiologists' performance on BC screening has shown to be enhanced, by leveraging AI-CAD system as decision support tool. As increasing implementation of commercial AI-CAD system, robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption, like other emerging and promising technologies. This requires to validate AI-CAD systems in BC screening on multiple, diverse and representative datasets and also to estimate the interface between reader and system. This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms. In this work, we will employ a commercially available AI-CAD tool based on deep-learning algorithms (IBM Watson Imaging AI Solution) to identify and characterize the suspicious breast lesions on mammograms. The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported. After AI post-processing, we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This is a single institutional retrospective cohort study of patients with mammographic examinations. All patients' data will be retrieved via the electronic patient database of our institution. Patient demographics, imaging and histological data, disease and treatment history will be recorded, including age at onset, details of chemotherapy, time interval of metastasis from diagnosis, surgery for the primary tumor, the length of survival, clinical outcome and so on.
Key inclusion criteria (3 listed)
- Women who had undergone standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views..
- Histopathology-proven diagnosis is available for patients with breast malignancy, including invasive breast cancer, carcinoma in situ, and borderline lesion et al.
- As reference standard of benign nature, results from pathology or clinical long-term follow-up (>=2 years) examinations are available for cases without breast malignancy.
Key exclusion criteria (4 listed)
- Patients with concurring lesions on mammograms that may influence subsequent AI post-process.
- Patients without available pathologic diagnosis or long-term follow-up (>=2 years) examinations.
- Patients who had undergone breast surgical intervention (e.g. lumpectomy and mammoplasty) prior to first mammography.
- Patients diagnosed with other kinds of malignancy, concurrent with metastasis or infiltration/invasion to breast.
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 |
|---|---|---|
| mammographystandard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views | Other | – |
Primary outcome
area under curve (AUC)
Time frame: 3 years
area under receiver operating characteristic (ROC) curve in percentage (%)
accuracy
Time frame: 3 years
proportion of true results(both true positives and true negatives) among whole instances
sensitivity
Time frame: 3 years
true positive rate in percentage(%) derived by ROC analysis
specificity
Time frame: 3 years
true negative rate in percentage (%) derived by ROC analysis
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Feb 2024.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Chinese University of Hong Kong at 1 site in Hong Kong.
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 3 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: withdrawn, as of the sponsor's last update on 7 Feb 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 Hong Kong. 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?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
area under curve (AUC) (time frame: 3 years). The study lists 4 primary outcomes in all. 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 7 Feb 2024.