Wide-Angle Tomosynthesis and AI in Diagnostic Mammography

NCT07491055 | Not Yet Recruiting

• 1 other identifier: 20250599-01H
• Study Type: observational
• Target: 1,400
• Locations: 0 countries
• Sites: N/A

Brief 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. 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.

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

Outcome Measures

Primary Outcomes (1)

• 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).

  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.

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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).

Sponsors & Collaborators

• Jean Seely: lead
• Varian, a Siemens Healthineers Company: collaborator

Related Publications (2)

• 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.

  PMID: 37541274 BACKGROUND

• Sasaki 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.

  PMID: 32052311 BACKGROUND

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Jean Seely, Physician

CONTACT

613-798-5555; jeseely@toh.ca

Rafael Ochoa Sanchez, PhD, Research Coordinator

CONTACT

613-798-5555; raochoa@ohri.ca

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Physician Medical Imaging

Study Record Dates

• First Submitted: March 16, 2026
• First Posted: March 24, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): September 1, 2029
• Study Completion (Estimated): December 31, 2029
• Last Updated: March 24, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share