Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)

NCT07075679 | Recruiting

• 2 other identifiers: 03062025IGA_LF_2024_022
• Study Type: interventional
• Target: 8,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Breast Cancer Screening; Artificial Intelligence (AI); Breast Cancer Screening and Diagnosis

Keywords

breast cancer; screening; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (2)

• Cancer Detection Rate

  up to 1 year after screening mammography

• Recall Rate

  up to 190 days after screening mammography

Study Arms (2)

Group with AI

EXPERIMENTAL

Asymptomatic women aged 45-69 participating in breast cancer screening programme, reading of mammograms by one radiologist with AI support.

Diagnostic Test: Group with AI iCAD version 3

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).

Diagnostic Test: Group without AI

Interventions

Group with AI iCAD version 3 DIAGNOSTIC_TEST

Reading mammograms by one radiologist with AI support

Group with AI

Group without AI DIAGNOSTIC_TEST

Standard double reading by two radiologists without AI.

Group without AI

Eligibility Criteria

• Age: 45 Years - 69 Years
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• age 45-69, asymptomatic woman participating in breast cancer screening programme

You may not qualify if:

• clinical signs of breast disease - indication for diagnostic mammography

Sponsors & Collaborators

• University Hospital Olomouc: lead

Study Sites (1)

University Hospital Olomouc

Olomouc, Czechia

RECRUITING

Related Publications (12)

• 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.

  PMID: 38597784 BACKGROUND

• 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.

  PMID: 28975929 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.

  PMID: 39227277 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.

  PMID: 38074781 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.

  PMID: 38416890 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.

  PMID: 36502459 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.

  PMID: 39775040 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.

  PMID: 38640824 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.

  PMID: 32876835 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.

  PMID: 37541274 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.

  PMID: 37690911 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.

  PMID: 40050619 RESULT

Related Links

• 1\. European Commission: Joint Research Centre, ECIBC at a Glance - European Commission Initiative on Breast Cancer, Publications Office of the European Union, 2023

MeSH Terms

Conditions

Disease; Breast Neoplasms

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Lucia Veverkova, MD

CONTACT

+420603181913; lucia.veverkova@fnol.cz

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 9, 2025
• First Posted: July 20, 2025
• Study Start: October 6, 2025
• Primary Completion (Estimated): October 5, 2028
• Study Completion (Estimated): October 5, 2028
• Last Updated: April 2, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CZ (1)