Breast Neoplasms DiagnosisNCT07491055
Wide-Angle Tomosynthesis and AI in Diagnostic Mammography
An observational study of Breast Neoplasms Diagnosis and Brest Cancer, run by Jean Seely.
Trial summary
Breast cancer remains the most commonly diagnosed cancer and a leading cause of cancer-related mortality among women globally. Timely and accurate detection is crucial for improving prognosis and survival outcomes. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Neoplasms Diagnosis, Brest Cancer
- How many people
- 1,400 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2026to 31 Dec 2029
- 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 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Participants will be adults receiving diagnostic breast imaging at The Ottawa Hospital Breast Imaging Center. The study population consists of consecutive patients referred for assessment of screen-detected abnormalities or clinical breast symptoms, who undergo routine diagnostic mammography performed on the Siemens MAMMOMAT B.brilliant system.
Who may be able to join
- Provides verbal consent to participate.
- Referred for diagnostic breast imaging at The Ottawa Hospital due to:
- Recall from a screening mammogram for a soft-tissue lesion, or
- Breast symptoms (e.g., palpable mass, nipple discharge) with last screening mammogram >6 months prior.
- Able to undergo wide-angle DBT and Insight 2D views on the Siemens MAMMOMAT B.brilliant system.
Who cannot join
- Presence of breast implants.
- History of breast surgery on the breast being evaluated.
- Required imaging views not obtained (wide-angle DBT + Insight 2D views).
- Unable or unwilling to complete the imaging procedure per standard protocol.
- Declines the use of AI on the mammography unit (patients who decline are imaged on another machine and not included).
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Evaluation of Wide-Angle Tomosynthesis and AI in Diagnostic Mammography at The Ottawa Hospital
Breast cancer remains the most commonly diagnosed cancer and a leading cause of cancer-related mortality among women globally. Timely and accurate detection is crucial for improving prognosis and survival outcomes. While digital mammography has long served as the gold standard for screening, it is limited by overlapping tissue structures, particularly in women with dense breasts, which can obscure malignancies or create false positives.
To address these limitations, digital breast tomosynthesis (DBT), especially wide-angle DBT, has been developed to offer three-dimensional imaging and reduce tissue overlap. Siemens' MAMMOMAT B.brilliant system, which incorporates wide-angle DBT, enhances spatial resolution and improves lesion conspicuity. This technology may offer significant benefits in diagnostic populations, where accuracy and confidence in imaging interpretation are crucial.
Read more
In parallel, artificial intelligence (AI) tools such as the Transpara system have been introduced to further improve mammographic interpretation. Previously the evaluation of Transpara in a sample of 310 Japanese women and found that while human readers outperformed AI in overall diagnostic performance, the system showed promising sensitivity levels, highlighting the potential of AI as a decision-support tool rather than a standalone reader.
More robust evidence is provided by the Mammography Screening with Artificial Intelligence (MASAI) trial, which assessed AI-supported screen reading in a controlled study of over 80,000 women. The trial found that AI-supported reading led to a comparable cancer detection rate as standard double reading (6.1 vs. 5.1 per 1000 participants) but reduced reading workload by 44.3% without increasing false positives or recall rates. A related analysis by the same team emphasized the capability of AI to triage exams effectively and highlighted that AI-flagged "extra high risk" mammograms accounted for a substantial portion (over 55%) of all screen-detected cancers, with a high positive predictive value.
Despite these encouraging findings, most studies have been limited to screening-based settings. There remains a lack of prospective evidence on the real-world diagnostic application of wide-angle DBT and AI in populations at higher risk, such as symptomatic patients or those recalled from screening. This represents a critical knowledge gap, especially given increasing concerns about radiologist workload and diagnostic delays.
The purpose of this prospective observational study is to evaluate the integration and diagnostic value of wide-angle tomosynthesis and AI (Transpara) in a clinical diagnostic setting. Specifically, it aims to assess their influence on radiologist confidence, diagnostic accuracy and the need for supplementary imaging. By addressing these questions, the study seeks to inform future implementation strategies that balance accuracy, efficiency, and clinical utility.
Read the detailed description
This prospective observational study evaluates the use of wide-angle digital breast tomosynthesis (DBT) and an artificial intelligence (AI) decision-support tool (Transpara) during diagnostic mammography at The Ottawa Hospital. All imaging performed in the study is part of routine clinical care and uses the Siemens MAMMOMAT B.brilliant system. The first 700 patients will have images interpreted without AI, and the next 700 with AI available to the radiologist. No additional imaging or procedures are required beyond standard care. The study will compare diagnostic confidence, need for supplementary imaging, biopsy outcomes, and overall workflow efficiency between the AI-supported and non-AI groups. Clinical follow-up for up to two years will be used to assess diagnostic accuracy and cancer outcomes.
- Conditions
- Breast Neoplasms Diagnosis, Brest Cancer
- Keywords
- Breast Cancer, Diagnostic Mammography, Digital Breast Tomosynthesis (DBT), Wide-Angle Tomosynthesis, Artificial Intelligence, AI-Assisted Imaging, Transpara AI, Siemens MAMMOMAT B.brilliant, Breast Imaging
- Medical subject headings
- Breast Neoplasms
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 |
|---|---|---|
| AI-OFF Cohort | Participants referred for diagnostic breast imaging who undergo wide-angle digital breast tomosynthesis (DBT) on the Siemens MAMMOMAT B.brilliant system, with radiologist interpretation performed without the use of the Transpara artificial intelligence decision-support tool. The first 700 consecutive patients enrolled will be included in this cohort. No procedures differ from standard clinical care. | |
| AI-ON Cohort | Participants referred for diagnostic breast imaging who undergo identical DBT imaging on the Siemens MAMMOMAT B.brilliant system, but radiologist interpretation is performed with Transpara artificial intelligence available as a decision-support tool. The subsequent 700 consecutive patients will be included in this cohort. Imaging and all clinical care remain standard of care; AI use does not alter patient management. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Confidence and Diagnostic Accuracy With and Without AI Support
Radiologist-reported diagnostic confidence when interpreting wide-angle DBT images, measured using a BI-RADS assessment based on standard clinical criteria. Confidence ratings and Diagnostic Accuracy will be compared between two cohorts: images interpreted without Transpara AI and Transpara AI. Confidence is assessed at the time of imaging interpretation, using structured electronic surveys and the BI-RADS score recorded in the clinical diagnostic report. This outcome reflects whether AI support influences radiologist confidence and interpretation performance.
At the 2-year follow-up, the study team will perform a chart-based review of each participant's clinical outcomes to determine final diagnostic accuracy (false negatives/positives).
Measured: 1- Day 1: Assessments at the diagnostic imaging visit (scan with or without AI). Biopsy collected. Radiologist reader confidence (BI-RADS). 2- Day 1 up to 6 months: Positive Predictive Value of Biopsy (PPV3). 3- 2 year follow-up: Diagnostic accuracy.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2026
- Study start1 Apr 2026 (estimated)
- Primary completion1 Sep 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Breast Neoplasms DiagnosisStudies 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
- Prospective
- Enrolment
- 1,400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jean Seely
- Sponsor type
- University, hospital or other
- Collaborators
- Varian, a Siemens Healthineers Company
- Responsible party
- Sponsor-investigator, Jean Seely, Ottawa Hospital Research Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07491055
- Sponsor's protocol ID
- 20250599-01H
- Status last verified
- Mar 2026
- First submitted
- 16 Mar 2026
- Last updated
- 24 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lang K, Josefsson V, Larsson AM, Larsson S, Hogberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023 Aug;24(8):936-944. doi: 10.1016/S1470-2045(23)00298-X.
PubMed 37541274Sasaki M, Tozaki M, Rodriguez-Ruiz A, Yotsumoto D, Ichiki Y, Terawaki A, Oosako S, Sagara Y, Sagara Y. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer. 2020 Jul;27(4):642-651. doi: 10.1007/s12282-020-01061-8. Epub 2020 Feb 12.
PubMed 32052311
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Wide-Angle Tomosynthesis and AI in Diagnostic Mammography at The Ottawa Hospital
- ClinicalTrials.gov ID
- NCT07491055
- Acronym / sponsor ID
- 20250599-01H
- Conditions
- Breast Neoplasms Diagnosis, Brest Cancer
- Keywords
- Breast Cancer, Diagnostic Mammography, Digital Breast Tomosynthesis (DBT), Wide-Angle Tomosynthesis, Artificial Intelligence, AI-Assisted Imaging, Transpara AI, Siemens MAMMOMAT B.brilliant
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,400 participants (estimated)
- Lead sponsor
- Jean SeelyUniversity, hospital or other
- Collaborators
- Varian, a Siemens Healthineers Company
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 1 Sep 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 24 Mar 2026 (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 remains the most commonly diagnosed cancer and a leading cause of cancer-related mortality among women globally. Timely and accurate detection is crucial for improving prognosis and survival outcomes. While digital mammography has long served as the gold standard for screening, it is limited by overlapping tissue structures, particularly in women with dense breasts, which can obscure malignancies or create false positives.
To address these limitations, digital breast tomosynthesis (DBT), especially wide-angle DBT, has been developed to offer three-dimensional imaging and reduce tissue overlap. Siemens' MAMMOMAT B.brilliant system, which incorporates wide-angle DBT, enhances spatial resolution and improves lesion conspicuity. This technology may offer significant benefits in diagnostic populations, where accuracy and confidence in imaging interpretation are crucial.
In parallel, artificial intelligence (AI) tools such as the Transpara system have been introduced to further improve mammographic interpretation. Previously the evaluation of Transpara in a sample of 310 Japanese women and found that while human readers outperformed AI in overall diagnostic performance, the system showed promising sensitivity levels, highlighting the potential of AI as a decision-support tool rather than a standalone reader.
More robust evidence is provided by the Mammography Screening with Artificial Intelligence (MASAI) trial, which assessed AI-supported screen reading in a controlled study of over 80,000 women. The trial found that AI-supported reading led to a comparable cancer detection rate as standard double reading (6.1 vs. 5.1 per 1000 participants) but reduced reading workload by 44.3% without increasing false positives or recall rates. A related analysis by the same team emphasized the capability of AI to triage exams effectively and highlighted that AI-flagged "extra high risk" mammograms accounted for a substantial portion (over 55%) of all screen-detected cancers, with a high positive predictive value.
Despite these encouraging findings, most studies have been limited to screening-based settings. There remains a lack of prospective evidence on the real-world diagnostic application of wide-angle DBT and AI in populations at higher risk, such as symptomatic patients or those recalled from screening. This represents a critical knowledge gap, especially given increasing concerns about radiologist workload and diagnostic delays.
The purpose of this prospective observational study is to evaluate the integration and diagnostic value of wide-angle tomosynthesis and AI (Transpara) in a clinical diagnostic setting. Specifically, it aims to assess their influence on radiologist confidence, diagnostic accuracy and the need for supplementary imaging. By addressing these questions, the study seeks to inform future implementation strategies that balance accuracy, efficiency, and clinical utility.
Detailed description
This prospective observational study evaluates the use of wide-angle digital breast tomosynthesis (DBT) and an artificial intelligence (AI) decision-support tool (Transpara) during diagnostic mammography at The Ottawa Hospital. All imaging performed in the study is part of routine clinical care and uses the Siemens MAMMOMAT B.brilliant system. The first 700 patients will have images interpreted without AI, and the next 700 with AI available to the radiologist. No additional imaging or procedures are required beyond standard care. The study will compare diagnostic confidence, need for supplementary imaging, biopsy outcomes, and overall workflow efficiency between the AI-supported and non-AI groups. Clinical follow-up for up to two years will be used to assess diagnostic accuracy and cancer outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-OFF Cohort | – | –Participants referred for diagnostic breast imaging who undergo wide-angle digital breast tomosynthesis (DBT) on the Siemens MAMMOMAT B.brilliant system, with radiologist interpretation performed without the use of the Transpara artificial intelligence decision-support tool. The first 700 consecutive patients enrolled will be included in this cohort. No procedures differ from standard clinical care. |
| AI-ON Cohort | – | –Participants referred for diagnostic breast imaging who undergo identical DBT imaging on the Siemens MAMMOMAT B.brilliant system, but radiologist interpretation is performed with Transpara artificial intelligence available as a decision-support tool. The subsequent 700 consecutive patients will be included in this cohort. Imaging and all clinical care remain standard of care; AI use does not alter patient management. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be adults receiving diagnostic breast imaging at The Ottawa Hospital Breast Imaging Center. The study population consists of consecutive patients referred for assessment of screen-detected abnormalities or clinical breast symptoms, who undergo routine diagnostic mammography performed on the Siemens MAMMOMAT B.brilliant system.
Key inclusion criteria (5 listed)
- Provides verbal consent to participate.
- Referred for diagnostic breast imaging at The Ottawa Hospital due to:
- Recall from a screening mammogram for a soft-tissue lesion, or
- Breast symptoms (e.g., palpable mass, nipple discharge) with last screening mammogram >6 months prior.
- Able to undergo wide-angle DBT and Insight 2D views on the Siemens MAMMOMAT B.brilliant system.
Key exclusion criteria (5 listed)
- Presence of breast implants.
- History of breast surgery on the breast being evaluated.
- Required imaging views not obtained (wide-angle DBT + Insight 2D views).
- Unable or unwilling to complete the imaging procedure per standard protocol.
- Declines the use of AI on the mammography unit (patients who decline are imaged on another machine and not included).
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
Diagnostic Confidence and Diagnostic Accuracy With and Without AI Support
Time frame: 1- Day 1: Assessments at the diagnostic imaging visit (scan with or without AI). Biopsy collected. Radiologist reader confidence (BI-RADS). 2- Day 1 up to 6 months: Positive Predictive Value of Biopsy (PPV3). 3- 2 year follow-up: Diagnostic accuracy.
Radiologist-reported diagnostic confidence when interpreting wide-angle DBT images, measured using a BI-RADS assessment based on standard clinical criteria. Confidence ratings and Diagnostic Accuracy will be compared between two cohorts: images interpreted without Transpara AI and Transpara AI. Confidence is assessed at the time of imaging interpretation, using structured electronic surveys and the BI-RADS score recorded in the clinical diagnostic report. This outcome reflects whether AI support influences radiologist confidence and interpretation performance. At the 2-year follow-up, the study team will perform a chart-based review of each participant's clinical outcomes to determine final diagnostic accuracy (false negatives/positives).
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 24 Mar 2026.
- Background: PubMed 37541274Lang K, Josefsson V, Larsson AM, Larsson S, Hogberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023 Aug;24(8):936-944. doi: 10.1016/S1470-2045(23)00298-X.
- Background: PubMed 32052311Sasaki M, Tozaki M, Rodriguez-Ruiz A, Yotsumoto D, Ichiki Y, Terawaki A, Oosako S, Sagara Y, Sagara Y. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer. 2020 Jul;27(4):642-651. doi: 10.1007/s12282-020-01061-8. Epub 2020 Feb 12.
- 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.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Neoplasms Diagnosis and Brest Cancer, run by Jean Seely.
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 5 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: opening soon, as of the sponsor's last update on 24 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
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 Jean Seely, Physician (613-798-5555 ext. 17522, jeseely@toh.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Confidence and Diagnostic Accuracy With and Without AI Support (time frame: 1- Day 1: Assessments at the diagnostic imaging visit (scan with or without AI). Biopsy collected. Radiologist reader confidence (BI-RADS). 2- Day 1 up to 6 months: Positive Predictive Value of Biopsy (PPV3). 3- 2 year follow-up: Diagnostic accuracy.). 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 NCT07491055, which also lists 2 linked publications.
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 24 Mar 2026.