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
|Condition or disease||Intervention/treatment||Phase|
|Breast Cancer||Other: AI screening modality Other: Conventional screening modality||Not Applicable|
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 (Clinical Trial)|
|Estimated Enrollment :||100000 participants|
|Intervention Model:||Parallel Assignment|
|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.|
|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|
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.
- Interval-cancer rate [ Time Frame: 43 months ]Women with interval cancer per 1000 screens
- Cancer-detection rate [ Time Frame: 15 months ]Women with screen-detected cancer per 1000 screens
- Recall rate [ Time Frame: 15 months ]Number of recalls per 1000 screens
- False-positive rate [ Time Frame: 15 months ]Women with false positive per 1000 screens
- Positive Predictive Value-1 [ Time Frame: 15 months ]Women with cancer for all recalls
- Sensitivity and specificity [ Time Frame: 43 months ]True and false-positive rate
- Cancer detection per cancer type [ Time Frame: 19 months ]Screen detection of cancer in relation to cancer type, size and stage
- Tumour biology of interval cancers [ Time Frame: 43 months ]Characterization of interval cancers per type, size and stage
- Screen-reading workload [ Time Frame: 19 months ]Number of screen-readings and number of consensus meetings
- Incremental cost-effectiveness ratio [ Time Frame: 43 months ]The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care
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
|Mammography Unit, Unilabs/Skane University Hospital|
|Malmö, Skane, Sweden, 20550|
|Principal Investigator:||Kristina Lång, MD PhD||Region Skåne|