Mammography Screening With Artificial Intelligence (MASAI)

Breast Cancer Clinical Trial

— MASAI  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04838756
• Other study ID #: 2020-04936
• Status: Active, not recruiting
• Phase: N/A
• Start date: April 12, 2021
• Est. completion date: April 12, 2025

Study information

• Verified date: March 2024
• Source: Region Skane
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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).

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 information / eligibility

• Status: Active, not recruiting
• Enrollment: 100000
• Est. completion date: April 12, 2025
• Est. primary completion date: November 12, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 40 Years to 74 Years

Eligibility

Inclusion Criteria:

Women eligible for population-based mammography screening. Exclusion Criteria: None.

Related Conditions & MeSH terms

• Breast Cancer

Intervention

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.
• Conventional screening modality
Screen exams will be read by two radiologists without the support of AI.

Locations

Country	Name	City	State
Sweden	Mammography Unit, Unilabs/Skane University Hospital	Malmö	Skane

Sponsors (3)

Lead Sponsor	Collaborator
Region Skane	Cancer Registry of Norway, Unilabs

Country where clinical trial is conducted

Sweden, 

Outcome

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