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Trial registered on ANZCTR
Registration number
ACTRN12624001432505
Ethics application status
Approved
Date submitted
16/09/2024
Date registered
6/12/2024
Date last updated
27/04/2025
Date data sharing statement initially provided
6/12/2024
Type of registration
Prospectively registered
Titles & IDs
Public title
BRAIx RCT: A study on the safety and efficacy of reader replacement with artificial intelligence in population breast cancer screening
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Scientific title
BRAIx RCT: A multi-state, single-blinded, randomised controlled trial assessing the impact of reader replacement with artificial intelligence on interval cancer rates in population breast cancer screening
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Secondary ID [1]
312852
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None
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Universal Trial Number (UTN)
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Trial acronym
BRAIx RCT
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Breast cancer screening
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Breast cancer
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Condition category
Condition code
Public Health
331514
331514
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0
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Health service research
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Cancer
331513
331513
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0
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Breast
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Intervention/exposure
Study type
Interventional
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Description of intervention(s) / exposure
Intervention: AI-integrated screening mammogram reading using investigational product; BRAIx AI Reader (Software as a Medical Device).
An AI reader (Deep Learning Classification Model) implemented to automatically analyse and assign a classification score to screening mammograms replacing the second radiologist reader in the independent double-reading of every mammogram, while maintaining the arbitration radiologist reader if needed. The first radiologist reader will not know the AI reader result in accordance with the current clinical routine (BreastScreen Australia National Accreditation Standards). If arbitration is required, the arbitration radiologist reader will be unblinded to both the AI and radiologist reader’s decision and have access to image annotations indicating Region of Interest of suspicion according to current clinical practice. The intervention will be implemented in the context of breast cancer screening, accordingly all women aged 40 and above who are eligible to participate in population-based mammography screening.
The approximate turnaround time for this mammogram double-reading procedure involving the AI reader, is 48 hours, which is well within the standard set by the BreastScreen Australia National Accreditation Standards, where screening reading results are provided to the client within 14 days.
A data log file procedure will be implemented to monitor adherence to the intervention if applicable, i.e. audit of patient medical records.
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Intervention code [1]
329402
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Early detection / Screening
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Comparator / control treatment
Control arm: Current screening mammogram reading standard
Screening mammograms are read independently by two radiologists (independent double reading) who recall the client for assessment if there is suspicion of cancer. If there is a discrepancy a third arbitration radiologist, who will have access to image annotations indicating Region of Interest of suspicion according to current clinical practice, will make the final decision.
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Control group
Active
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Outcomes
Primary outcome [1]
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To assess the effect of AI-integrated mammogram reading compared with the current mammogram reading standard (independent reading by two radiologists and a third if discrepancy) on interval cancer rate (ICR).
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Assessment method [1]
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Audit of patient medical records. Data collection & assessment methods for the RCT will utilise Routinely Collected health Data (RCD) from the BreastScreen services. The RCT will utilise these existing comprehensive databases to gather trial related RCD applicable to evaluating trial outcomes namely, screening and assessment outcomes, the reading results of individual radiologist readers and ICR. Data format received as a combination of Excel spreadsheets/CSV files and DICOM Files to account for non-image and image data elements respectively.
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Timepoint [1]
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24 months post final participant enrolment
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Secondary outcome [1]
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To assess the effectiveness of AI-integrated mammogram reading in breast cancer population screening in terms of accuracy and efficiency combined. Accordingly, effectiveness will be assessed as a composite secondary outcome evaluating the following measures together: - screen detected cancer rate (SDCR) - recall rate (RR) - false positive rate (FPR) - arbitration read rate (ARR).
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Assessment method [1]
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Prior to commencing recruitment update to Audit of patient medical records. Data collection & assessment methods for the RCT will utilise Routinely Collected health Data (RCD) from the BreastScreen services. Specifically assessing the following RCD metrics between the intervention and control arms to the BreastScreen Australia National Accreditation Standards (NAS.: - screen detected cancer rate (SDCR) - recall rate (RR) - arbitration read rate (ARR).
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Timepoint [1]
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Every 28-days during trial intervention and 28-days post intervention
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Secondary outcome [2]
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To assess the safety of AI-integrated mammogram reading by monitoring the screen detected cancer rate (SDCR)
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Assessment method [2]
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Audit of patient medical records. Data collection & assessment methods for the RCT will utilise Routinely Collected health Data (RCD) from the BreastScreen services. Compare SDCR in the intervention and control arms and versus the BreastScreen Australia National Accreditation Standards (NAS).
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Timepoint [2]
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Every 6-months during trial intervention.
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Secondary outcome [3]
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Positive Predictive Value will be derived from True Positive Rates and False Positive Rates to derive how accurate the AI reader is in the intervention compared to the human readers in the intervention arm.
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Assessment method [3]
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Audit of patient medical records. Data collection & assessment methods for the RCT will utilise Routinely Collected health Data (RCD) from the BreastScreen services. True Positive Rates and False Positive Rates will be used to calculate the AI Reader's PPV with the formula: True Positives / (True Positives + False Positives).
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Timepoint [3]
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24 months post final participant enrolment
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Secondary outcome [4]
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Sensitivity (derived from True Positive rate and False Negative rate) and Specificity (derived from False Positive rate and True Negative rate) will be assessed as a composite secondary outcome in the form of area under the curve (AUC) in a receiver operating characteristic (ROC) curve to determine accuracy of the AI reader in the intervention compared to the human readers in the intervention arm. The AUC is an effective measure of accuracy because it takes into account the trade-off between sensitivity and specificity.
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Assessment method [4]
441948
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Audit of patient medical records. Data collection & assessment methods for the RCT will utilise Routinely Collected health Data (RCD) from the BreastScreen services. True Positive Rates, False Negative Rates, False Positive Rates and True Negative Rates will be used to plot sensitivity versus 1-specificity for different cut-off points. The closer the curve is to the upper left corner, the higher the AUC and therefore the accuracy of the reader.
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Timepoint [4]
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24 months post final participant enrolment
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Eligibility
Key inclusion criteria
To be included in the study, subjects must meet the following criteria:
- Women eligible to participate in population-based mammography screening in Victoria and South Australia.
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Minimum age
40
Years
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Maximum age
No limit
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Sex
Females
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Can healthy volunteers participate?
Yes
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Key exclusion criteria
No exclusion criteria applied.
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Study design
Purpose of the study
Diagnosis
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Allocation to intervention
Randomised controlled trial
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Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
central randomisation by computer
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Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Computerised sequence generation.
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Masking / blinding
Blinded (masking used)
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Who is / are masked / blinded?
The people receiving the treatment/s
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Intervention assignment
Parallel
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Other design features
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Phase
Not Applicable
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Type of endpoint/s
Safety/efficacy
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Statistical methods / analysis
The RCT is designed to assess with 80% power and at a 5% significance level that the interval cancer rate (ICR) in the AI reading model intervention arm is non-inferior to the standard reading model control arm at a non-inferiority margin of 20% of the current rate.
ICR in the intervention arm compared to the control arm will be assessed by calculating 95% confidence intervals (one-sided and two-sided) for the difference in proportions. Non-inferiority of the intervention will be established if the one-sided interval does not cross the 20% margin. Superiority of the intervention will be established if the two-sided interval does not contain zero.
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Recruitment
Recruitment status
Not yet recruiting
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Date of first participant enrolment
Anticipated
21/04/2025
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Actual
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Date of last participant enrolment
Anticipated
3/08/2026
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Actual
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Date of last data collection
Anticipated
24/07/2028
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Actual
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Sample size
Target
205000
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
SA,VIC
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Funding & Sponsors
Funding source category [1]
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Government body
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Name [1]
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Department of Health and Aged Care - Medical Research Future Fund (MRFF)
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Address [1]
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Country [1]
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Australia
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Primary sponsor type
Other
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Name
ST VINCENT'S INSTITUTE OF MEDICAL RESEARCH
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Address
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Country
Australia
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Secondary sponsor category [1]
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None
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Name [1]
319709
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Address [1]
319709
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Country [1]
319709
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Ethics approval
Ethics application status
Approved
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Ethics committee name [1]
316030
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The Royal Melbourne Hospital Human Research Ethics Committee
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Ethics committee address [1]
316030
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https://www.thermh.org.au/research/researchers/ethics
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Ethics committee country [1]
316030
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Australia
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Date submitted for ethics approval [1]
316030
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31/01/2024
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Approval date [1]
316030
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26/02/2024
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Ethics approval number [1]
316030
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Summary
Brief summary
This research study is investigating the safety and efficacy of using artificial intelligence (AI) in Australian breast cancer population screening by determining if using AI improves mammogram reading accuracy and timeliness and reduces reading workload. Who is it for? You may be eligible for this study if you are a woman aged 40 years and over who is eligible to participate in population-based mammography screening in Victoria and South Australia. Study details Participants will be randomly allocated to have their mammograms read by an AI-integrated system, or by the current mammogram reading standard of care (independent reading by two blinded radiologists and a third if discrepancy). The AI-integrated system will replace one of the independent radiologists with an AI reader (BRAIx AI Reader) while maintaining the arbitration radiologist reader if needed. Data will be collected on screen-detected breast cancer rates and radiologist workload. It is hoped that findings from this study will help researchers evaluate the clinical utility of implementing AI within breast cancer population screening systems in an Australian setting.
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Trial website
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Trial related presentations / publications
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Public notes
To contact the study team please use the following contact details: BreastScreen Victoria Contact Centre Phone: 13 20 50 Email: braixrct@breastscreen.org.au BreastScreen Victoria central contact number will be answered by BreastScreen call centre staff that will be trained to ensure the calls are appropriately forwarded to the project team, including PI.
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Contacts
Principal investigator
Name
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A/Prof Helen ML Frazer
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Address
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BreastScreen Victoria; PO Box 542, Carlton VIC 3053
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Country
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Australia
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Phone
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+61 13 20 50
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Fax
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Email
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[email protected]
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Contact person for public queries
Name
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Katrina Kunicki
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Address
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St Vincent's Institute of Medical Research; 9 Princes St Fitzroy VIC 3065
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Country
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Australia
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Phone
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+613 13 20 50
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Fax
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Email
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[email protected]
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Contact person for scientific queries
Name
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Katrina Kunicki
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Address
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St Vincent's Institute of Medical Research; 9 Princes St Fitzroy VIC 3065
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Country
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Australia
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Phone
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+613 9231 2480
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Fax
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Email
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[email protected]
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Data sharing statement
Will the study consider sharing individual participant data?
No
No IPD sharing reason/comment:
All participant data will be de-identified and study findings reported at a cohort or aggregate level.
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
Documents added manually
No documents have been uploaded by study researchers.
Documents added automatically
No additional documents have been identified.
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