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Mammography Screening With Artificial Intelligence (MASAI) (MASAI)

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ClinicalTrials.gov Identifier: NCT04838756
Recruitment Status : Enrolling by invitation
First Posted : April 9, 2021
Last Update Posted : April 15, 2021
Sponsor:
Collaborators:
Unilabs
Cancer Registry of Norway
Information provided by (Responsible Party):
Region Skane

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).

Condition or disease Intervention/treatment Phase
Breast Cancer Other: AI screening modality Other: Conventional screening modality Not Applicable

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.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 100000 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single (Participant)
Masking Description: Participants have the possibility to opt-out. If they do not opt-out, neither the participant nor the nurse performing the screen exam will know to what study arm the participant was allocated. The radiologist reading the screen exam will however not be blinded to allocation information.
Primary Purpose: Screening
Official Title: A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study
Actual Study Start Date : April 12, 2021
Estimated Primary Completion Date : November 12, 2024
Estimated Study Completion Date : April 12, 2025

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Mammography

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.




Primary Outcome Measures :
  1. Interval-cancer rate [ Time Frame: 43 months ]
    Women with interval cancer per 1000 screens


Secondary Outcome Measures :
  1. Cancer-detection rate [ Time Frame: 15 months ]
    Women with screen-detected cancer per 1000 screens

  2. Recall rate [ Time Frame: 15 months ]
    Number of recalls per 1000 screens

  3. False-positive rate [ Time Frame: 15 months ]
    Women with false positive per 1000 screens

  4. Positive Predictive Value-1 [ Time Frame: 15 months ]
    Women with cancer for all recalls

  5. Sensitivity and specificity [ Time Frame: 43 months ]
    True and false-positive rate

  6. Cancer detection per cancer type [ Time Frame: 19 months ]
    Screen detection of cancer in relation to cancer type, size and stage

  7. Tumour biology of interval cancers [ Time Frame: 43 months ]
    Characterization of interval cancers per type, size and stage

  8. Screen-reading workload [ Time Frame: 19 months ]
    Number of screen-readings and number of consensus meetings

  9. Incremental cost-effectiveness ratio [ Time Frame: 43 months ]
    The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care



Information from the National Library of Medicine

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Ages Eligible for Study:   40 Years to 74 Years   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

Women eligible for population-based mammography screening.

Exclusion Criteria:

None.


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04838756


Locations
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Sweden
Mammography Unit, Unilabs/Skane University Hospital
Malmö, Skane, Sweden, 20550
Sponsors and Collaborators
Region Skane
Unilabs
Cancer Registry of Norway
Investigators
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Principal Investigator: Kristina Lång, MD PhD Region Skåne
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Responsible Party: Region Skane
ClinicalTrials.gov Identifier: NCT04838756    
Other Study ID Numbers: 2020-04936
First Posted: April 9, 2021    Key Record Dates
Last Update Posted: April 15, 2021
Last Verified: April 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: IPD could be considered to be shared in future collaborations.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Region Skane:
Mammography Screening
Artificial Intelligence