Artificial Intelligence in Large-scale Breast Cancer Screening

Study Overview
=================
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.

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

Conditions
-----------------
Breast Neoplasm Female

Intervention / Treatment
-----------------
* Diagnostic Test: AI CAD
* Diagnostic Test: Radiologist reading


Participation Criteria
=================
Eligibility 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

Ages Eligible for Study
-----------------
Minimum Age: 40 Years
Maximum Age: 74 Years

Sexes Eligible for Study
-----------------
Female

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Single Group Assignment
Interventional 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


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Standard of Care<br>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<br>* 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)<br>|
| Experimental: AI CAD combination<br>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<br>* 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.<br>* Other names: Lunit INSIGHT MMG;Diagnostic Test: Radiologist reading<br>* 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)<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Incident breast cancer | Breast cancer diagnosis by pathologist | At Screening | 
| Incident breast cancer | Breast cancer diagnosis by pathologist | Within 12 months after screening | 
| Incident breast cancer | Breast cancer diagnosis by pathologist | Within 23 months after screening | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Reader flagging | Radiologist or AICAD assessing the mammograms as suspicious or not suspicious for malignancy | At screening | 
| Consensus recall | A decision by the consensus discussion to recall the woman for further work-up | At screening | 
| Tissue sampling | Biopsy or fine needle aspiration performed | At screening | 
| Process failure | Failure of the AI CAD software to generate AI scores | At screening | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Artificial Intelligence, Breast Cancer, Mammography, Screening