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| ID | Type | Description | Link |
|---|---|---|---|
| EPM 2020-00487 | Other Identifier | Ethical Review Authority (Sweden) | |
| K 2020-0807 | Other Identifier | Karolinska University Hospital |
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| Name | Class |
|---|---|
| Capio Sankt Görans Hospital | OTHER |
| Lunit Inc. | INDUSTRY |
| Karolinska Institutet | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Standard of Care | Active Comparator | 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. |
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| AI CAD combination | Experimental | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI CAD | Diagnostic Test | 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. |
| 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 |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Fredrik Strand, MD PhD | Karolinska University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Capio St Göran Hospital | Stockholm | 11219 | Sweden |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40100021 | Derived | Dembrower KE, Crippa A, Eklund M, Strand F. Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader. Radiology. 2025 Mar;314(3):e242566. doi: 10.1148/radiol.242566. | |
| 37690911 | Derived | Dembrower K, Crippa A, Colon E, Eklund M, Strand F; ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health. 2023 Oct;5(10):e703-e711. doi: 10.1016/S2589-7500(23)00153-X. Epub 2023 Sep 8. |
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To the extent allowed by source institution, legal agreements, and applicable laws and regulations
At study start
Anyone can access study protocol and SAP.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | May 16, 2022 | Mar 10, 2023 | Prot_SAP_002.pdf |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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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.
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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.
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| Radiologist reading | Diagnostic Test | 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) |
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| 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 |
| D017437 |
| Skin and Connective Tissue Diseases |