Mammography Screening With Artificial Intelligence (MASAI)
- Conditions
- Breast Cancer
- Interventions
- Other: Conventional screening modalityOther: AI screening modality
- Registration Number
- NCT04838756
- Lead Sponsor
- 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).
- 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.
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- Female
- Target Recruitment
- 100000
Women eligible for population-based mammography screening.
None.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Control arm Conventional screening modality Conventional mammography screening (standard of care) Intervention arm AI screening modality AI-integrated mammography screening
- Primary Outcome Measures
Name Time Method Interval-cancer rate 43 months Women with interval cancer per 1000 screens
- Secondary Outcome Measures
Name Time Method Tumour biology of interval cancers 43 months Characterization of interval cancers per type, size and stage
Screen-reading workload 19 months Number of screen-readings and number of consensus meetings
Positive Predictive Value-1 15 months Women with cancer for all recalls
Recall rate 15 months Number of recalls per 1000 screens
Incremental cost-effectiveness ratio 43 months The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care
Cancer-detection rate 15 months Women with screen-detected cancer per 1000 screens
Cancer detection per cancer type 19 months Screen detection of cancer in relation to cancer type, size and stage
False-positive rate 15 months Women with false positive per 1000 screens
Sensitivity and specificity 43 months True and false-positive rate
Trial Locations
- Locations (1)
Mammography Unit, Unilabs/Skane University Hospital
🇸🇪Malmö, Skane, Sweden