Mammography Screening With Artificial Intelligence (MASAI)

Region Skane

Status: Active, not recruiting

Conditions: Breast Cancer

Treatments: Other: Conventional screening modality; Other: AI screening modality

Study type: Interventional

Funder types: Other

Identifiers

NCT04838756

2020-04936

Details and patient eligibility

About

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).

Full 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.

Enrollment: 100,000 patients

Sex: Female

Ages: 40 to 74 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

Women eligible for population-based mammography screening.

Exclusion criteria

None.

Trial design

Primary purpose: Screening

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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100,000 participants in 2 patient groups

Intervention arm

Experimental group

Description:

AI-integrated mammography screening

Treatment:

Other: AI screening modality

Control arm

Experimental group

Description:

Conventional mammography screening (standard of care)

Treatment:

Other: Conventional screening modality