Artificial Intelligence in Large-scale Breast Cancer Screening (ScreenTrustCAD)

Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD

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.

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.

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.

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.

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.

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.

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)

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