Mammography Screening With Artificial Intelligence (MASAI) (MASAI)

A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study

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).

Study Overview

• Status: Active, not recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Other: AI screening modality; Other: Conventional screening modality

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.

• Study Type: Interventional
• Enrollment (Actual): 100000
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Sweden
  • Skane
    • Malmö, Skane, Sweden, 20550
      • Mammography Unit, Unilabs/Skane University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 38 years to 72 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

Women eligible for population-based mammography screening.

Exclusion Criteria:

None.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Interval-cancer rate	Women with interval cancer per 1000 screens	43 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cancer-detection rate	Women with screen-detected cancer per 1000 screens	15 months
Recall rate	Number of recalls per 1000 screens	15 months
False-positive rate	Women with false positive per 1000 screens	15 months
Positive Predictive Value-1	Women with cancer for all recalls	15 months
Sensitivity and specificity	True and false-positive rate	43 months
Cancer detection per cancer type	Screen detection of cancer in relation to cancer type, size and stage	19 months
Tumour biology of interval cancers	Characterization of interval cancers per type, size and stage	43 months
Screen-reading workload	Number of screen-readings and number of consensus meetings	19 months
Incremental cost-effectiveness ratio	The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care	43 months

Collaborators and Investigators

• Sponsor: Region Skane
• Collaborators: Cancer Registry of Norway; Unilabs
• Investigators: Principal Investigator: Kristina Lång, MD PhD, Region Skane

Study record dates

Study Major Dates

• Study Start (Actual): April 12, 2021
• Primary Completion (Estimated): November 12, 2024
• Study Completion (Estimated): April 12, 2025

Study Registration Dates

• First Submitted: April 6, 2021
• First Submitted That Met QC Criteria: April 8, 2021
• First Posted (Actual): April 9, 2021

Study Record Updates

• Last Update Posted (Actual): March 12, 2024
• Last Update Submitted That Met QC Criteria: March 8, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Mammography Screening
• Other Study ID Numbers: 2020-04936

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: IPD could be considered to be shared in future collaborations.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No