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| ID | Type | Description | Link |
|---|---|---|---|
| IGA_LF_2024_022 | Other Grant/Funding Number | Internal Grant of the Palacky University |
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A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.
In the intervention group, the first reading of the screening mammogram will be performed by one radiologist with AI support. After this AI-assisted first reading is completed and recorded for the study, a standard second reading will be carried out by another radiologist. This ensures that the legal requirement for double reading in the breast cancer screening program is maintained.
In the control group, mammograms will be evaluated according to the current standard practice - that is, independently by two radiologists without AI support.
Participants will be divided to the two groups based on a randomization scheme that determines specific days for evaluation with AI (Group 1) and without AI (Group 2). The randomization ensures equal distribution of weekdays between the two groups to minimize bias due to variability in daily workflow, diagnostic/screening ratios, or other operational factors.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group with AI | Experimental | Asymptomatic women aged 45-69 participating in breast cancer screening programme, reading of mammograms by one radiologist with AI support. |
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| Group without AI | Other | Asymptomatic women aged 45-69 participating in breast cancer screening programme, reading of mammograms by two radiologists without AI (current practice). |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Group with AI iCAD version 3 | Diagnostic Test | Reading mammograms by one radiologist with AI support |
|
| Measure | Description | Time Frame |
|---|---|---|
| Further assessment rate | The proportion of women udergoing follow-up examinations within 0-190 days after screening mammography. | up to 190 days after screening mammmography |
| Measure | Description | Time Frame |
|---|---|---|
| Cancer Detection Rate | The number of cancers detected per 1,000 women screened. | up to 1 year after screening mammography |
| Recall Rate | The proportion of women recalled for follow-up examinations withing 1-190 days after screening mammography. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Lucia Veverkova, MD | Contact | +420603181913 | lucia.veverkova@fnol.cz |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital Olomouc | Recruiting | Olomouc | Czechia |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38597784 | Background | Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, Mikalsen KO, Lund-Hanssen H, Solli HS, Silberhorn M, Sulheim AO, Auensen S, Nygard JF, Hofvind S. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell. 2024 May;6(3):e230375. doi: 10.1148/ryai.230375. | |
| 28975929 |
| Label | URL |
|---|---|
| 1\. European Commission: Joint Research Centre, ECIBC at a Glance - European Commission Initiative on Breast Cancer, Publications Office of the European Union, 2023 | View source |
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| Group without AI | Diagnostic Test | Standard double reading by two radiologists without AI. |
|
| up to 190 days after screening mammography |
| Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub 2017 Oct 4. |
| 39227277 | Result | Tudos Z, Veverkova L, Baxa J, Hartmann I, Ctvrtlik F. The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma. Best Pract Res Clin Endocrinol Metab. 2025 Jan;39(1):101923. doi: 10.1016/j.beem.2024.101923. Epub 2024 Aug 23. |
| 38074781 | Result | McDonald ES, Conant EF. Can AI Reduce the Harms of Screening Mammography? Radiol Artif Intell. 2023 Oct 25;5(6):e230304. doi: 10.1148/ryai.230304. eCollection 2023 Nov. No abstract available. |
| 38416890 | Result | Letter H, Peratikos M, Toledano A, Hoffmeister J, Nishikawa R, Conant E, Shisler J, Maimone S, Diaz de Villegas H. Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience. J Breast Imaging. 2023 May 22;5(3):258-266. doi: 10.1093/jbi/wbad015. |
| 36502459 | Result | Dahlblom V, Dustler M, Tingberg A, Zackrisson S. Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence. Eur Radiol. 2023 May;33(5):3754-3765. doi: 10.1007/s00330-022-09316-y. Epub 2022 Dec 11. |
| 39775040 | Result | Eisemann N, Bunk S, Mukama T, Baltus H, Elsner SA, Gomille T, Hecht G, Heywang-Kobrunner S, Rathmann R, Siegmann-Luz K, Tollner T, Vomweg TW, Leibig C, Katalinic A. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med. 2025 Mar;31(3):917-924. doi: 10.1038/s41591-024-03408-6. Epub 2025 Jan 7. |
| 38640824 | Result | Diaz O, Rodriguez-Ruiz A, Sechopoulos I. Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects. Eur J Radiol. 2024 Jun;175:111457. doi: 10.1016/j.ejrad.2024.111457. Epub 2024 Apr 16. |
| 32876835 | Result | Lang K, Dustler M, Dahlblom V, Akesson A, Andersson I, Zackrisson S. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol. 2021 Mar;31(3):1687-1692. doi: 10.1007/s00330-020-07165-1. Epub 2020 Sep 2. |
| 37541274 | Result | Lang K, Josefsson V, Larsson AM, Larsson S, Hogberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023 Aug;24(8):936-944. doi: 10.1016/S1470-2045(23)00298-X. |
| 37690911 | Result | 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. |
| 40050619 | Result | Chang YW, Ryu JK, An JK, Choi N, Park YM, Ko KH, Han K. Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study. Nat Commun. 2025 Mar 6;16(1):2248. doi: 10.1038/s41467-025-57469-3. |
| ID | Term |
|---|---|
| D004194 | Disease |
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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