NCT07491055

Wide-Angle Tomosynthesis and AI in Diagnostic Mammography

Not yet recruiting Last updated 24 March 2026

What this trial tests

trial in Breast Neoplasms Diagnosis in 1,400 participants. Not yet recruiting.

Timeline

1 April 2026

Primary endpoint
1 September 2029

31 December 2029

Quick facts

Lead sponsor	Jean Seely
Status	Not yet recruiting
Study type	OBSERVATIONAL
Enrollment	1,400
Start date	1 April 2026
Primary completion	1 September 2029
Estimated completion	31 December 2029

Conditions studied

• Breast Neoplasms Diagnosis — all drugs for Breast Neoplasms Diagnosis →
• Brest Cancer — all drugs for Brest Cancer →

Sponsor

Jean Seely

Who can join

18 and older, female only, with Breast Neoplasms Diagnosis or Brest Cancer. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.