Mammography Screening With Artificial Intelligence (MASAI)

NCT ID: NCT04838756

Last Updated: 2025-04-04

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 100000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-04-12
• Study Completion Date: 2025-05-07

Brief 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 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.

Conditions

Breast Cancer

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: SINGLE

Study Groups

Intervention arm

AI-integrated mammography screening

Group Type: EXPERIMENTAL

AI screening modality

Intervention Type: OTHER

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.

Control arm

Conventional mammography screening (standard of care)

Group Type: EXPERIMENTAL

Conventional screening modality

Intervention Type: OTHER

Screen exams will be read by two radiologists without the support of AI.

Interventions

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.

Intervention Type: OTHER

Conventional screening modality

Screen exams will be read by two radiologists without the support of AI.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

Women eligible for population-based mammography screening.

Exclusion Criteria

None.

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 74 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Unilabs

UNKNOWN

Sponsor Role: collaborator

Norwegian Institute of Public Health

OTHER_GOV

Sponsor Role: collaborator

Region Skane

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Kristina Lång, MD PhD

Role: PRINCIPAL_INVESTIGATOR

Region Skåne

Locations

Mammography Unit, Unilabs/Skane University Hospital

Malmo, Skåne County, Sweden

Countries

Sweden

References

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.

Reference Type: RESULT

PMID: 37541274 (View on PubMed)

Hernstrom 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.

Reference Type: RESULT

PMID: 39904652 (View on PubMed)

Other Identifiers

2020-04936

Identifier Type: -

Identifier Source: org_study_id