NCT05968157

Brief Summary

Accurate risk assessment is essential for the success of population screening programs and early detection efforts in breast cancer. Mirai is a new deep learning model based on full resolution mammograms. Mirai is a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and found to be significantly more accurate than the Tyrer-Cuzick model, a current clinical standard. The primary aim of this study is to prospectively quantify the clinical benefit (i.e. MRI/CEM cancer detection rate) of Mirai-based guidelines and to compare them to the current standard of care.

  1. 1.Conduct a prospective study where patients who are identified as high risk by Mirai guidelines are invited to receive supplemental MRI within 12 months.
  2. 2.Compare cancer outcomes between patients only identified as high risk by Mirai and patients identified as high risk by existing guidelines The secondary aim is to study the impact of new guidelines by race and ethnicity, to ensure equitable improvements in cancer screening.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
200

participants targeted

Target at P50-P75 for not_applicable breast-cancer

Timeline
17mo left

Started Feb 2024

Typical duration for not_applicable breast-cancer

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress63%
Feb 2024Sep 2027

First Submitted

Initial submission to the registry

July 21, 2023

Completed
11 days until next milestone

First Posted

Study publicly available on registry

August 1, 2023

Completed
6 months until next milestone

Study Start

First participant enrolled

February 4, 2024

Completed
2.3 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2026

Expected
1.3 years until next milestone

Study Completion

Last participant's last visit for all outcomes

September 1, 2027

Last Updated

January 7, 2026

Status Verified

January 1, 2026

Enrollment Period

2.3 years

First QC Date

July 21, 2023

Last Update Submit

January 6, 2026

Conditions

Keywords

Breast CancerScreeningArtificial Intelligence

Outcome Measures

Primary Outcomes (1)

  • CDR Mirai Assessment versus CDR Traditional High Risk Screening

    Cancer detection rate from breast MRI following Mirai assessment of high risk on a screening mammogram performed less than 1 year ago and compared with established CDR in traditional high risk screening.

    1.5 years (duration of patient recruitment and outcome data collection)

Secondary Outcomes (1)

  • Cancer development within study population versus general population of average risk women

    1.5 years (duration of patient recruitment and outcome data collection)

Study Arms (2)

High Risk Participants--MIRAI

EXPERIMENTAL

Patients who are deemed high risk on standard breast screening mammogram by the MIRAI model

Diagnostic Test: Breast MRIDevice: MIRAI

High Risk Participants--non-MIRAI

ACTIVE COMPARATOR

Patients who are deemed high risk by Tyrer-Cuzick model but not MIRAI

Diagnostic Test: Breast MRI

Interventions

Breast MRIDIAGNOSTIC_TEST

Supplemental MRI (in addition to standard of care MRI).

High Risk Participants--MIRAIHigh Risk Participants--non-MIRAI
MIRAIDEVICE

Artificial intelligence software

High Risk Participants--MIRAI

Eligibility Criteria

Age40 Years+
Sexfemale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Women who were identified as high risk on the retrospective study (dating from 2017-2025) using MIRAI will be recruited and consented for the prospective study
  • Women over 40 years of age identified as high risk according to traditional guidelines will also be potentially eligible for this study
  • Following consent and enrollment in the study, a participant will subsequently receive the following:
  • These patients will be invited to receive a supplemental MRI examination currently considered the most sensitive test for breast cancer detection.
  • Any positive diagnosis on MRI will be followed by biopsy to confirm 'truth" of diagnosis.
  • To be selected, a given record must include the following:
  • A report of a routine screening mammogram or diagnostic mammogram, and availability of the DICOM images from that report with the PACS system.
  • Reports of all follow up screening and diagnostic studies documented on PACS.
  • Some may have interventional procedures (as long as all of these are done at one of Umass sites) and documentation of these biopsy results in the hospitals EHR.

You may not qualify if:

  • Under age 40. Women under 40 years are not routinely xrayed with a mammogram.
  • Xray breast cancer screening imaging study that has artifacts, corruption, or other image quality degradation.
  • Pregnant patients because they do not routinely receive screening mammogram
  • Adult male patients with breast cancer

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

UMass Medical School

Worcester, Massachusetts, 01655, United States

RECRUITING

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Officials

  • Mohammed Salman Shazeeb, PhD

    UMass Chan Medical School

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Sara Schiller, MPH

CONTACT

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NON RANDOMIZED
Masking
NONE
Purpose
SCREENING
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Director - Image Processing & Analysis Core; Director of Preclinical MRI & Co-Director of Scientific Affairs (Advanced MRI Center); UMass Chan Medical School

Study Record Dates

First Submitted

July 21, 2023

First Posted

August 1, 2023

Study Start

February 4, 2024

Primary Completion (Estimated)

June 1, 2026

Study Completion (Estimated)

September 1, 2027

Last Updated

January 7, 2026

Record last verified: 2026-01

Data Sharing

IPD Sharing
Will not share

Locations