Breast Parenchimal EnhancementNCT06838130
AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)
An observational study of Breast Parenchimal Enhancement and Artificial Intelligence (AI), run by Link Campus University at 1 site in Italy.
Trial summary
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Parenchimal Enhancement, Artificial Intelligence (AI)
- How many people
- 213 participants
- Where
- 1 siteItaly
- Study dates
- 1 May 2022to 30 Jun 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 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Women aged 18 years and older who underwent CEM for diagnostic or surveillance purposes. Patients with recorded BPE levels and BI-RADS breast density classification. Relational database containing structured data for correlation matrix analysis and AI model training.
Who may be able to join
None listed.
Who cannot join
- Patients with prior breast cancer treatment that could alter BPE.
- Incomplete imaging or missing classification data.
- Contraindications to contrast-enhanced imaging.
Trial locations and contacts
1 site in 1 country.
About this study
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| patiens underwent CEM | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correlation between breast density, BPE, and age using AI-driven analysis.
Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression.
Measured: Data analysis within 12 months of study completion.
Secondary outcomes (3)
AI-based optimization of breast density and BPE classification
Evaluating the performance of neural networks in predicting BPE levels across different breast density categories.
Measured: Within 12 months of study completion
Comparative performance of multiple linear regression vs. AI models.
Assessing the accuracy of traditional statistical methods versus ANN-based predictions in explaining variance in BPE values.
Measured: Within 12 months of study completion.
Mean Squared Error (MSE) and explained variance in predictive models
Analyzing the error rates and variance explained by different AI models compared to multiple linear regression.
Measured: Within 12 months of study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Feb 2025
- Study start1 May 2022
- Primary completion30 Jun 2023
- Study completion30 Jun 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Breast Parenchimal EnhancementStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 213 participants (actual)
Sponsor and investigators
- Lead sponsor
- Link Campus University
- Sponsor type
- University, hospital or other
- Collaborators
- University of Campania Luigi Vanvitelli
- Responsible party
- Principal investigator, Graziella di Grezia, Link Campus University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06838130
- Sponsor's protocol ID
- T_6_2025
- Status last verified
- Oct 2026
- First submitted
- 15 Feb 2025
- Last updated
- 2 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Di Grezia G, Nazzaro A, Schiavone L, Cisternino E, Galiano A, Cuccurullo V, Gatta G. AI-assisted Decision-support Framework for Breast Density and Background Parenchymal Enhancement Assessment in Contrast-enhanced Mammography. Curr Med Imaging. 2026;22:e15734056444815. doi: 10.2174/0115734056444815260715111252.
PubMed 42515921
Study at a glance
From the registry record, last updated by the sponsor on 2 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)
- ClinicalTrials.gov ID
- NCT06838130
- Acronym / sponsor ID
- T_6_2025
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 213 participants (actual)
- Lead sponsor
- Link Campus UniversityUniversity, hospital or other
- Collaborators
- University of Campania Luigi Vanvitelli
- Study start
- 1 May 2022 (actual)
- Primary completion
- 30 Jun 2023 (actual)
- Study completion
- 30 Jun 2023 (actual)
- First posted
- 20 Feb 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.
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patiens underwent CEM | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Women aged 18 years and older who underwent CEM for diagnostic or surveillance purposes. Patients with recorded BPE levels and BI-RADS breast density classification. Relational database containing structured data for correlation matrix analysis and AI model training.
Key inclusion criteria (0 listed)
Key exclusion criteria (3 listed)
- Patients with prior breast cancer treatment that could alter BPE.
- Incomplete imaging or missing classification data.
- Contraindications to contrast-enhanced imaging.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Correlation between breast density, BPE, and age using AI-driven analysis.
Time frame: Data analysis within 12 months of study completion.
Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression.
3 secondary outcomes
AI-based optimization of breast density and BPE classification
Time frame: Within 12 months of study completion
Comparative performance of multiple linear regression vs. AI models.
Time frame: Within 12 months of study completion.
Mean Squared Error (MSE) and explained variance in predictive models
Time frame: Within 12 months of study completion
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Nov 2026.
- Background: PubMed 42515921Di Grezia G, Nazzaro A, Schiavone L, Cisternino E, Galiano A, Cuccurullo V, Gatta G. AI-assisted Decision-support Framework for Breast Density and Background Parenchymal Enhancement Assessment in Contrast-enhanced Mammography. Curr Med Imaging. 2026;22:e15734056444815. doi: 10.2174/0115734056444815260715111252.
- Individual participant dataSharing planned: No
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 Parenchimal Enhancement and Artificial Intelligence (AI), run by Link Campus University 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 0 inclusion and 3 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: finished, as of the sponsor's last update on 2 Nov 2026. 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 is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Correlation between breast density, BPE, and age using AI-driven analysis. (time frame: Data analysis within 12 months of study completion.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06838130, which also lists 1 linked publication.
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 2 Nov 2026.