Mammography Screening With Artificial Intelligence (MASAI) (MASAI)

March 27, 2026 updated by: Region Skane

A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study

The purpose of this randomized controlled trial is to assess whether AI can improve the efficacy of mammography screening, by adapting single and double reading based on AI derived cancer-risk scores and to use AI as a decision support in the screen reading, compared with conventional mammography screening (double reading without AI).

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

European guidelines recommend that mammography exams in breast cancer screening are read by two breast radiologists to ensure a high sensitivity. Double reading is, however, resource demanding and still results in missed cancers. Computer-aided detection based on AI has been shown to have similar accuracy as an average breast radiologist. AI can be used as decision support by highlighting suspicious findings in the image as well as a means to triage screen exams according to risk of malignancy.

Eligible women will be randomized (1:1) to the intervention (AI-integrated mammography screening) or control arm (conventional mammography screening). In the intervention arm, exams will be analysed with AI and triaged into two groups based on risk of malignancy. Low risk exams will be single read and high risk exams will be double read. The high risk group will contain appx. 10% of the screening population. Within the high-risk group, exams with the highest 1% risk will by default be recalled by the readers with the exception of obvious false positives. AI risk scores and Computer-Aided Detection (CAD)-marks of suspicious calcifications and masses are provided to the reader(s). In the control arm, screen exams are double read without AI (standard of care). Considering the interplay of number of interval cancers and workload, the study will be considered successful if the interval-cancer rate in the intervention arm is not more than 20% larger than in the control arm. If the interval-cancer rate is statistically and clinically significantly lower in the intervention arm than in the control arm, AI-integrated mammography screening will be considered superior to conventional mammography screening.

Study Type

Interventional

Enrollment (Actual)

100000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Sweden
    • Skåne County
      • Malmo, Skåne County, Sweden, 20550
        • Mammography Unit, Unilabs/Skane University Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

36 years to 70 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

Women eligible for population-based mammography screening.

Exclusion Criteria:

None.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Screening
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Intervention arm
AI-integrated mammography screening
Other: AI screening modality
Screen exam will be analysed with an AI system (Transpara, ScreenPoint, Nijmegen, The Netherlands) that assigns exams with a cancer-risk score from 1 to 10, as well as presenting CAD-marks at suspicious findings. Exams with risk score 1-9 will be single read and exam with score 10 will be double read. Risk scores and CAD-marks are provided to the reader(s). The reader(s) will decide whether to recall the woman for work-up or not (as per standard of care). In addition, exams with the highest 1% risk will by default be recalled with the exception of obvious false positives.
Experimental: Control arm
Conventional mammography screening (standard of care)
Other: Conventional screening modality
Screen exams will be read by two radiologists without the support of AI.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Interval-cancer rate
Time Frame: 43 months
Women with interval cancer per 1000 screens
43 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Cancer-detection rate
Time Frame: 15 months
Women with screen-detected cancer per 1000 screens
15 months
Recall rate
Time Frame: 15 months
Number of recalls per 1000 screens
15 months
False-positive rate
Time Frame: 15 months
Women with false positive per 1000 screens
15 months
Positive Predictive Value-1
Time Frame: 15 months
Women with cancer for all recalls
15 months
Sensitivity and specificity
Time Frame: 43 months
True and false-positive rate
43 months
Cancer detection per cancer type
Time Frame: 19 months
Screen detection of cancer in relation to cancer type, size and stage
19 months
Tumour biology of interval cancers
Time Frame: 43 months
Characterization of interval cancers per type, size and stage
43 months
Screen-reading workload
Time Frame: 19 months
Number of screen-readings and number of consensus meetings
19 months
Incremental cost-effectiveness ratio
Time Frame: 43 months
The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care
43 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Region Skane

Collaborators

Norwegian Institute of Public Health
Unilabs

Investigators

  • Principal Investigator: Kristina Lång, MD PhD, Region Skane

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 12, 2021

Primary Completion (Actual)

December 7, 2024

Study Completion (Actual)

August 12, 2025

Study Registration Dates

First Submitted

April 6, 2021

First Submitted That Met QC Criteria

April 8, 2021

First Posted (Actual)

April 9, 2021

Study Record Updates

Last Update Posted (Actual)

April 2, 2026

Last Update Submitted That Met QC Criteria

March 27, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2020-04936

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

IPD could be considered to be shared in future collaborations.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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