Breast CancerMASAINCT04838756
Mammography Screening With Artificial Intelligence (MASAI)
A study testing AI screening modality and Conventional screening modality in people with Breast Cancer, run by Region Skane at 1 site in Sweden.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 40 to 74 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer
- What is being tested?
- AI screening modality and Conventional screening modality
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100,000 participants
- Where
- 1 siteSweden
- Study dates
- 12 Apr 2021to 12 Aug 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 40 to 74 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Women eligible for population-based mammography screening.
Who cannot join
- None.
Trial locations and contacts
1 site in 1 country.
Sweden1 site
Mammography Unit, Unilabs/Skane University Hospital
Malmö, Skåne County, 20550
About this study
Official title: 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).
Read the 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.
- Conditions
- Breast Cancer
- Being tested
- AI screening modality, Conventional screening modality
- Keywords
- Mammography Screening, Artificial Intelligence
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI screening modalityOther
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.
Conventional screening modalityOther
Screen exams will be read by two radiologists without the support of AI.
| Study group | Role | What they receive |
|---|---|---|
| Intervention arm | Experimental | AI-integrated mammography screening Receives: Other: AI screening modality |
| Control arm | Experimental | Conventional mammography screening (standard of care) Receives: Other: Conventional screening modality |
What is being measured
The primary outcome is the main question the study is designed to answer.
Interval-cancer rate
Women with interval cancer per 1000 screens
Measured: 43 months
Secondary outcomes (9)
Cancer-detection rate
Women with screen-detected cancer per 1000 screens
Measured: 15 months
Recall rate
Number of recalls per 1000 screens
Measured: 15 months
False-positive rate
Women with false positive per 1000 screens
Measured: 15 months
Positive Predictive Value-1
Women with cancer for all recalls
Measured: 15 months
Sensitivity and specificity
True and false-positive rate
Measured: 43 months
Cancer detection per cancer type
Screen detection of cancer in relation to cancer type, size and stage
Measured: 19 months
Tumour biology of interval cancers
Characterization of interval cancers per type, size and stage
Measured: 43 months
Screen-reading workload
Number of screen-readings and number of consensus meetings
Measured: 19 months
Incremental cost-effectiveness ratio
The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care
Measured: 43 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Apr 2021
- Study start12 Apr 2021
- Primary completion7 Dec 2024
- Study completion12 Aug 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 100,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Region Skane
- Sponsor type
- University, hospital or other
- Collaborators
- Unilabs, Norwegian Institute of Public Health
- Responsible party
- Sponsor
- Investigators
- Kristina Lång, MD PhD, Principal investigator, Region Skane
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04838756
- Sponsor's protocol ID
- 2020-04936
- Acronym
- MASAI
- Status last verified
- Mar 2026
- First submitted
- 6 Apr 2021
- Last updated
- 2 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IPD could be considered to be shared in future collaborations.
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 37541274Hernstrom V, Josefsson V, Sartor H, Schmidt D, Larsson AM, Hofvind S, Andersson I, Rosso A, Hagberg O, Lang K. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health. 2025 Mar;7(3):e175-e183. doi: 10.1016/S2589-7500(24)00267-X. Epub 2025 Feb 3.
PubMed 39904652Gommers J, Hernstrom V, Josefsson V, Sartor H, Schmidt D, Hjelmgren A, Larsson AM, Hofvind S, Andersson I, Rosso A, Hagberg O, Lang K. Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial. Lancet. 2026 Jan 31;407(10527):505-514. doi: 10.1016/S0140-6736(25)02464-X.
PubMed 41620232
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study
- ClinicalTrials.gov ID
- NCT04838756
- Acronym / sponsor ID
- MASAI / 2020-04936
- Conditions
- Breast Cancer
- Keywords
- Mammography Screening, Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 100,000 participants (actual)
- Lead sponsor
- Region SkaneUniversity, hospital or other
- Collaborators
- Unilabs, Norwegian Institute of Public Health
- Study start
- 12 Apr 2021 (actual)
- Primary completion
- 7 Dec 2024 (actual)
- Study completion
- 12 Aug 2025 (actual)
- First posted
- 9 Apr 2021 (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.
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).
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 design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention arm | Experimental | Other: AI screening modalityAI-integrated mammography screening |
| Control arm | Experimental | Other: Conventional screening modalityConventional mammography screening (standard of care) |
Who may qualify
- Ages
- 40 to 74 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Women eligible for population-based mammography screening.
Key exclusion criteria (1 listed)
- None.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI screening modalityScreen 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. | Other | Intervention arm |
| Conventional screening modalityScreen exams will be read by two radiologists without the support of AI. | Other | Control arm |
Primary outcome
Interval-cancer rate
Time frame: 43 months
Women with interval cancer per 1000 screens
9 secondary outcomes
Cancer-detection rate
Time frame: 15 months
Recall rate
Time frame: 15 months
False-positive rate
Time frame: 15 months
Positive Predictive Value-1
Time frame: 15 months
Sensitivity and specificity
Time frame: 43 months
Cancer detection per cancer type
Time frame: 19 months
Tumour biology of interval cancers
Time frame: 43 months
Screen-reading workload
Time frame: 19 months
Incremental cost-effectiveness ratio
Time frame: 43 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2026.
- Results publication: 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.
- Results publication: PubMed 39904652Hernstrom V, Josefsson V, Sartor H, Schmidt D, Larsson AM, Hofvind S, Andersson I, Rosso A, Hagberg O, Lang K. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health. 2025 Mar;7(3):e175-e183. doi: 10.1016/S2589-7500(24)00267-X. Epub 2025 Feb 3.
- Derived from this study: PubMed 41620232Gommers J, Hernstrom V, Josefsson V, Sartor H, Schmidt D, Hjelmgren A, Larsson AM, Hofvind S, Andersson I, Rosso A, Hagberg O, Lang K. Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial. Lancet. 2026 Jan 31;407(10527):505-514. doi: 10.1016/S0140-6736(25)02464-X.
- Individual participant dataSharing planned: No. IPD could be considered to be shared in future collaborations.
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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. This study is not recruiting at the moment.
Kristina Lång, MD PhD principal investigator
Region Skane
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI screening modality and Conventional screening modality in people with Breast Cancer, run by Region Skane at 1 site in Sweden.
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 1 inclusion and 1 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: finished, as of the sponsor's last update on 2 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Interval-cancer rate (time frame: 43 months). 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 NCT04838756, which also lists 3 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 2 Apr 2026.