BreastBAKERNCT07156006
Breast Arterial Calcifications as an Imaging Biomarker of Cardiovascular Risk
An observational study of Breast and Cardiovascular Calcification, run by IRCCS Policlinico S. Donato at 1 site in Italy.
Trial summary
The goal of this observational study is to assess if there is an association between the presence of BAC and traditional cardiovascular risk factors and validate a Convolutional Neural Network (CNN) for the automatic segmentation of Breast Arterial Calcifications (BAC) in mammographic images. Read more about this study
- Who is it for?
- Ages 40 years and older, women only
- Conditions the study is for
- Breast, Cardiovascular Calcification
- What is being tested?
- Mammography
- How many people
- 149 participants
- Where
- 1 siteItaly
- Study dates
- 11 Sep 2020to 29 Apr 2024
- 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 40 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of women aged more than 40 years who have consented to undergo mammography screening. Participants will be recruited from individuals attending mammography screening programs at our institute.
Who may be able to join
- Female participants. Consent to undergo mammography screening. Agreement to participate in brain MRI for a subset of the study.
Who cannot join
- Male participants. Age below 40. Inability or unwillingness to undergo mammography screening. Contraindications for brain MRI, including the presence of pacemaker, intracranial ferromagnetic vascular clips, intraocular metallic fragments, severe claustrophobia, inability to maintain a supine position, involuntary movements, or pregnancy.
- Known history of breast cancer. Previous reductive breast surgery.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Automatic Quantification of Breast Arterial Calcifications as an Imaging Biomarker of Cardiovascular Risk (the BAKER Study)
The goal of this observational study is to assess if there is an association between the presence of BAC and traditional cardiovascular risk factors and validate a Convolutional Neural Network (CNN) for the automatic segmentation of Breast Arterial Calcifications (BAC) in mammographic images. This study focuses on understanding the potential of BAC as an imaging biomarker for cardiovascular risk.
The main questions it aims to answer are:
Read more
- Is there an association between the presence of BAC and traditional cardiovascular risk factors?
- Can a CNN accurately segment BAC in mammographic images?
- What is the correlation between BAC and White Matter Hyperintensities (WMH) detected through brain MRI?
Participants in this study will be individuals who undergo mammographic screening. The main tasks participants will be asked to do include providing consent for participation and having mammographic images and a blood sample taken. The study will use a comparison group, comparing individuals with BAC to those without BAC, to assess potential effects on cardiovascular risk.
Read the detailed description
Association between BAC and Cardiovascular Risk Factors
- Traditional cardiovascular risk factors will be analyzed, and statistical tests (t-test or U de Mann-Whitney) will be employed based on the data distribution.
- Multivariate analysis will be performed to determine the independent association between BAC load and cardiovascular risk factors.
- Linear regression will assess the relationship between BAC load and Framingham score, aiming for a clinically applicable model.
Development of CNN for BAC Segmentation
- Mammographic images will be acquired using a digital full-field mammography system as per clinical practice.
- Two experienced operators will manually segment the images to create a dataset for training, validation, and testing the CNN.
- About 60% of the images acquired in the first year will be used for training, and the remaining 40% will form the validation and test datasets.
- Performance evaluation of the CNN will be conducted using the Sørensen similarity index, Bland-Altman analysis, and Free Response Receiver Operating Characteristic (FROC).
Association between BAC and White Matter Hyperintensities (WMH)
- A subset of participants will undergo brain MRI to assess WMH.
- The association between BAC quantity in mammography and WMH load in MRI will be evaluated using machine learning techniques.
- Other small vessel disease markers, such as lacunar infarcts and microbleeds, will also be analyzed.
Patient Enrollment:
The study aims to enroll 600 women, considering a 1:1 ratio between cases and controls. With an estimated 50% adherence rate, it anticipates evaluating 1500 women over two years.
This comprehensive study integrates the development of advanced imaging techniques with clinical correlations to explore the potential of BAC as an imaging biomarker for cardiovascular risk assessment.
- Conditions
- Breast, Cardiovascular Calcification
- Being tested
- Mammography
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MammographyDiagnostic test
Also called Blood test
Participants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices and blood sampling.
| Study group | Role | What they receive |
|---|---|---|
| BAC Group | Outpatients presenting in our department for annual mammography will be screened and selected for BAC presence.
Mammographic Imaging:
Participants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices.
The acquired mammographic images will serve as the basis for the development and testing of the Convolutional Neural Network (CNN) for Breast Arterial Calcifications (BAC) segmentation.
Venous Blood Sample Collection:
For each participants, a venous blood sample will be collected and traditional cardiovascular risk factors (such as age, hypertension, hyperlipidemia) will be recorded. Receives: Diagnostic Test: Mammography | |
| Control Group | Outpatients presenting in our department for annual mammography will be screened and matched for age and breast density to BAC Group.
Mammographic Imaging:
Participants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices.
The acquired mammographic images will serve as the basis for the development and testing of the Convolutional Neural Network (CNN) for Breast Arterial Calcifications (BAC) segmentation.
Venous Blood Sample Collection:
For each participants, a venous blood sample will be collected and traditional cardiovascular risk factors (such as age, hypertension, hyperlipidemia) will be recorded. Receives: Diagnostic Test: Mammography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Association Between BAC and Cardiovascular Risk Factors
Methodology: This aspect of the study aims to assess the association between the burden of BAC and traditional cardiovascular risk factors. Parametric and non-parametric tests will be employed to evaluate differences in BAC burden based on the presence or absence of traditional cardiovascular and gynecological risk factors.
Implications: A positive association between BAC burden and cardiovascular risk factors may emphasize the potential of BAC as a biomarker for cardiovascular risk.
Measured: One observation at the time of the mammography examination. Total time frame: 1 day.
Secondary outcomes (1)
Diagnostic Performance of CNN Detection and Quantification of BAC on Mammograms
To assess the performance and accuracy of the Convolutional Neural Network (CNN) in automatically segmenting BAC from mammographic images. The assessment will be based on metrics such as the Sørensen similarity index, Bland-Altman analysis, and Free Response Receiver Operating Characteristic (FROC) analysis. The CNN's ability to reliably and accurately identify and delineate BAC regions in the mammograms will be the secondary focus of the outcome assessment.
Measured: One observation at the time of the mammography examination. Total time frame: 1 day.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2025
- Study start11 Sep 2020
- Primary completion29 Apr 2024
- Study completion29 Apr 2024
Why it stopped: PI ceased activity
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BreastStudies of the same condition, open and under way.
- More trials run by IRCCS Policlinico S. DonatoThe 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 149 participants (actual)
Sponsor and investigators
- Lead sponsor
- IRCCS Policlinico S. Donato
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Francesco Sardanelli, IRCCS Policlinico S. Donato
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07156006
- Sponsor's protocol ID
- BAKER
- Other IDs
- 90/INT/2020
- Acronym
- BAKER
- Status last verified
- Sep 2025
- First submitted
- 26 Jan 2024
- Last updated
- 4 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Quantification of Breast Arterial Calcifications as an Imaging Biomarker of Cardiovascular Risk (the BAKER Study)
- ClinicalTrials.gov ID
- NCT07156006
- Acronym / sponsor ID
- BAKER / BAKER
- Conditions
- Breast, Cardiovascular Calcification
- Study type
- Observational
- Recruitment status
- TerminatedReason stopped: PI ceased activity
- Enrolment
- 149 participants (actual)
- Lead sponsor
- IRCCS Policlinico S. DonatoUniversity, hospital or other
- Study start
- 11 Sep 2020 (actual)
- Primary completion
- 29 Apr 2024 (actual)
- Study completion
- 29 Apr 2024 (actual)
- First posted
- 4 Sep 2025 (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.
The goal of this observational study is to assess if there is an association between the presence of BAC and traditional cardiovascular risk factors and validate a Convolutional Neural Network (CNN) for the automatic segmentation of Breast Arterial Calcifications (BAC) in mammographic images. This study focuses on understanding the potential of BAC as an imaging biomarker for cardiovascular risk.
The main questions it aims to answer are:
- Is there an association between the presence of BAC and traditional cardiovascular risk factors?
- Can a CNN accurately segment BAC in mammographic images?
- What is the correlation between BAC and White Matter Hyperintensities (WMH) detected through brain MRI?
Participants in this study will be individuals who undergo mammographic screening. The main tasks participants will be asked to do include providing consent for participation and having mammographic images and a blood sample taken. The study will use a comparison group, comparing individuals with BAC to those without BAC, to assess potential effects on cardiovascular risk.
Detailed description
Association between BAC and Cardiovascular Risk Factors
- Traditional cardiovascular risk factors will be analyzed, and statistical tests (t-test or U de Mann-Whitney) will be employed based on the data distribution.
- Multivariate analysis will be performed to determine the independent association between BAC load and cardiovascular risk factors.
- Linear regression will assess the relationship between BAC load and Framingham score, aiming for a clinically applicable model.
Development of CNN for BAC Segmentation
- Mammographic images will be acquired using a digital full-field mammography system as per clinical practice.
- Two experienced operators will manually segment the images to create a dataset for training, validation, and testing the CNN.
- About 60% of the images acquired in the first year will be used for training, and the remaining 40% will form the validation and test datasets.
- Performance evaluation of the CNN will be conducted using the Sørensen similarity index, Bland-Altman analysis, and Free Response Receiver Operating Characteristic (FROC).
Association between BAC and White Matter Hyperintensities (WMH)
- A subset of participants will undergo brain MRI to assess WMH.
- The association between BAC quantity in mammography and WMH load in MRI will be evaluated using machine learning techniques.
- Other small vessel disease markers, such as lacunar infarcts and microbleeds, will also be analyzed.
Patient Enrollment:
The study aims to enroll 600 women, considering a 1:1 ratio between cases and controls. With an estimated 50% adherence rate, it anticipates evaluating 1500 women over two years.
This comprehensive study integrates the development of advanced imaging techniques with clinical correlations to explore the potential of BAC as an imaging biomarker for cardiovascular risk assessment.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| BAC Group | – | Diagnostic Test: MammographyOutpatients presenting in our department for annual mammography will be screened and selected for BAC presence. Mammographic Imaging: Participants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices. The acquired mammographic images will serve as the basis for the development and testing of the Convolutional Neural Network (CNN) for Breast Arterial Calcifications (BAC) segmentation. Venous Blood Sample Collection: For each participants, a venous blood sample will be collected and traditional cardiovascular risk factors (such as age, hypertension, hyperlipidemia) will be recorded. |
| Control Group | – | Diagnostic Test: MammographyOutpatients presenting in our department for annual mammography will be screened and matched for age and breast density to BAC Group. Mammographic Imaging: Participants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices. The acquired mammographic images will serve as the basis for the development and testing of the Convolutional Neural Network (CNN) for Breast Arterial Calcifications (BAC) segmentation. Venous Blood Sample Collection: For each participants, a venous blood sample will be collected and traditional cardiovascular risk factors (such as age, hypertension, hyperlipidemia) will be recorded. |
Who may qualify
- Ages
- 40 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of women aged more than 40 years who have consented to undergo mammography screening. Participants will be recruited from individuals attending mammography screening programs at our institute.
Key inclusion criteria (1 listed)
- Female participants. Consent to undergo mammography screening. Agreement to participate in brain MRI for a subset of the study.
Key exclusion criteria (2 listed)
- Male participants. Age below 40. Inability or unwillingness to undergo mammography screening. Contraindications for brain MRI, including the presence of pacemaker, intracranial ferromagnetic vascular clips, intraocular metallic fragments, severe claustrophobia, inability to maintain a supine position, involuntary movements, or pregnancy.
- Known history of breast cancer. Previous reductive breast surgery.
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 |
|---|---|---|
| MammographyAlso: Blood testParticipants will undergo mammographic imaging using a digital full-field mammography system, following standard clinical practices and blood sampling. | Diagnostic test | BAC Group, Control Group |
Primary outcome
Association Between BAC and Cardiovascular Risk Factors
Time frame: One observation at the time of the mammography examination. Total time frame: 1 day.
Methodology: This aspect of the study aims to assess the association between the burden of BAC and traditional cardiovascular risk factors. Parametric and non-parametric tests will be employed to evaluate differences in BAC burden based on the presence or absence of traditional cardiovascular and gynecological risk factors. Implications: A positive association between BAC burden and cardiovascular risk factors may emphasize the potential of BAC as a biomarker for cardiovascular risk.
1 secondary outcome
Diagnostic Performance of CNN Detection and Quantification of BAC on Mammograms
Time frame: One observation at the time of the mammography examination. Total time frame: 1 day.
Results and safety
The study was stopped early (reason given: PI ceased activity). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Sep 2025.
- Individual participant dataSharing planned: No
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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 and Cardiovascular Calcification, run by IRCCS Policlinico S. Donato at 1 site in Italy.
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 1 inclusion and 2 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: stopped early, as of the sponsor's last update on 4 Sep 2025. 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 Italy. 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 stopped early (reason given: PI ceased activity). Results are not available in the source record.
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?
Association Between BAC and Cardiovascular Risk Factors (time frame: One observation at the time of the mammography examination. Total time frame: 1 day.). 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 4 Sep 2025.