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Trial record 1 of 1 for:    NCT04778670
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Artificial Intelligence in Large-scale Breast Cancer Screening (ScreenTrustCAD)

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ClinicalTrials.gov Identifier: NCT04778670
Recruitment Status : Not yet recruiting
First Posted : March 3, 2021
Last Update Posted : March 3, 2021
Sponsor:
Collaborators:
Capio Sankt Görans Hospital
Lunit Inc.
Karolinska Institutet
Information provided by (Responsible Party):
Fredrik Strand, Karolinska University Hospital

Brief Summary:
This is a prospective clinical trial following a paired screen-positive design, with the aims to assess the performance of an artificial intelligence (AI) computer-aided detection (CAD) algorithm as an independent reader, in addition to two radiologists, of screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.

Condition or disease Intervention/treatment Phase
Breast Neoplasm Female Diagnostic Test: AI CAD Diagnostic Test: Radiologist reading Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 55000 participants
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Intervention Model Description: This is a prospective clinical trial following a paired screen-positive design (Pepe, Alonzo; 2001), with the aims to assess the performance of an AI algorithm combined with radiologists(s) compared to standard-of-care being two radiologists assessing screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.
Masking: Triple (Participant, Care Provider, Investigator)
Masking Description: Positive disease status is ascertained by pathology-verified breast cancer. Disease status is not known to any of the actors (except for the outcomes assessor by necessity). AI decision is not known by the care provider radiologists until they have made their decisions. In the subsequent consensus discussion where a decision is made to recall or not to recall a woman, the AI decision is known. After AI decision has been recorded and outcomes have been assessed, the investigators will have full information on outcomes and AI decisions.
Primary Purpose: Diagnostic
Official Title: Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD
Estimated Study Start Date : March 2021
Estimated Primary Completion Date : December 2022
Estimated Study Completion Date : December 2024

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Breast Cancer

Arm Intervention/treatment
Active Comparator: Standard of Care
Standard of Care means all examinations will receive a flagging decision by: first reader and second reader radiologist as usual. However, in this paired design all participants will belong to both arms.
Diagnostic Test: Radiologist reading
Standard of care, each radiologist will assess the mammography examination, making a binary flagging decision (flag the examination to continue to consensus discussion, or not)

Experimental: AI CAD combination
AI CAD combination in the primary end-point means the combination of the flagging decision of the first reader and AI CAD; in the secondary end-points it means any combination of AI alone, or AI in combination with first, second and both readers.
Diagnostic Test: AI CAD
The Lunit INSIGHT MMG will be used as the AI CAD in our study. Initially, version 1.6.1.1 will be installed. The software version will be continuously updated with subsequent software releases, after confirming in a historic calibration dataset that the performance is improved. The operating point will be set based on a historic calibration dataset to attain a joint sensitivity of breast cancer detection of AI and first reader which is 2% higher than for first and second reader.
Other Name: Lunit INSIGHT MMG

Diagnostic Test: Radiologist reading
Standard of care, each radiologist will assess the mammography examination, making a binary flagging decision (flag the examination to continue to consensus discussion, or not)




Primary Outcome Measures :
  1. Incident breast cancer [ Time Frame: At Screening ]
    Breast cancer diagnosis by pathologist

  2. Incident breast cancer [ Time Frame: Within 12 months after screening ]
    Breast cancer diagnosis by pathologist

  3. Incident breast cancer [ Time Frame: Within 23 months after screening ]
    Breast cancer diagnosis by pathologist


Secondary Outcome Measures :
  1. Reader flagging [ Time Frame: At screening ]
    Radiologist or AICAD assessing the mammograms as suspicious or not suspicious for malignancy

  2. Consensus recall [ Time Frame: At screening ]
    A decision by the consensus discussion to recall the woman for further work-up

  3. Tissue sampling [ Time Frame: At screening ]
    Biopsy or fine needle aspiration performed

  4. Process failure [ Time Frame: At screening ]
    Failure of the AI CAD software to generate AI scores



Information from the National Library of Medicine

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Ages Eligible for Study:   40 Years to 74 Years   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Participants in regular population-based breast cancer screening at Capio St Göran Hospital

Exclusion Criteria:

  • Incomplete exam (complete exam: mediolateral oblique and craniocaudal images of Left and Right breast)
  • Breast implant
  • Complete mastectomy (excluded from screening positive group)
  • Participant in surveillance program for prior breast cancer

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04778670


Contacts
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Contact: Fredrik Strand, MD PhD +46 8 51770000 fredrik.strand@ki.se

Locations
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Sweden
Capio St Göran Hospital
Stockholm, Sweden, 11219
Contact: Anders Byström, MD    +46 858701000    anders.bystrom@capiostgoran.se   
Principal Investigator: Fredrik Strand, MD PhD         
Sub-Investigator: Karin Dembrower, MD         
Sponsors and Collaborators
Karolinska University Hospital
Capio Sankt Görans Hospital
Lunit Inc.
Karolinska Institutet
Investigators
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Principal Investigator: Fredrik Strand, MD PhD Karolinska University Hospital
  Study Documents (Full-Text)

Documents provided by Fredrik Strand, Karolinska University Hospital:
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Responsible Party: Fredrik Strand, Registrar (biträdande överläkare), Karolinska University Hospital
ClinicalTrials.gov Identifier: NCT04778670    
Other Study ID Numbers: STGKS001
EPM 2020-00487 ( Other Identifier: Ethical Review Authority (Sweden) )
K 2020-0807 ( Other Identifier: Karolinska University Hospital )
First Posted: March 3, 2021    Key Record Dates
Last Update Posted: March 3, 2021
Last Verified: February 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: To the extent allowed by source institution, legal agreements, and applicable laws and regulations
Supporting Materials: Study Protocol
Statistical Analysis Plan (SAP)
Time Frame: At study start
Access Criteria: Anyone can access study protocol and SAP.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Fredrik Strand, Karolinska University Hospital:
Artificial Intelligence
Breast Cancer
Mammography
Screening
Additional relevant MeSH terms:
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Breast Neoplasms
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases