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Exploring the Potential of Artificial Intelligence for Earlier Breast Cancer Detection: A Retrospective Multi-Reader Study Based on AI-Assisted Mammographic Interpretation (EARLIEST-AI)

NCT07556900 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 785

Last updated 2026-04-29

No results posted yet for this study

Summary

EARLIEST-AI Study type: Two-phase, multi-reader, blinded retrospective observational study based on data from a single clinic.

The primary objective of the study is to assess the sensitivity and specificity of radiologists and Artificial Intelligence (AI) in interpreting mammographic examinations for breast cancer detection in a scenario where no previous examinations are available, and to compare the diagnostic performance of radiologists with and without AI support.

The secondary objectives of the study are to assess the independent diagnostic performance of Computer Aided Detection (CAD) software, including sensitivity and specificity in identifying histopathologically confirmed breast cancer cases; to assess inter-reader and intra-reader variability in interpretation with and without AI support; to assess the agreement between AI outputs and histopathological findings; and to assess the impact of mammogram technical parameters on AI performance.

Time frame: Review of a subset of mammograms randomly selected from those performed between January 1, 2012 and December 31, 2024. Imaging findings were classified according to the Breast Imaging Reporting and Data System (BI-RADS).

Inclusion criteria:

1. Female patients aged 30 years or older.
2. One or more mammograms performed between January 1, 2012 and December 31, 2024.
3. Meets one of the following criteria:

3.1 Histopathologically confirmed diagnosis of breast cancer within 6 months of mammogram, 3.2 or two consecutive mammograms with BI-RADS 1 or BI-RADS 2.

Exclusion criteria:

1. Mammograms of poor quality or artifacts that do not allow for reliable assessment.
2. History of breast surgery or previous breast cancer treatment that has significantly altered breast morphology.
3. Data deficiencies, including:

3.1. lack of previous histopathological data 3.2. or lack of available follow-up data.

Conditions

  • Breast Cancer
  • AI (Artificial Intelligence)
  • Early Detection of Cancer

Sponsors & Collaborators

  • Hera-MI, SAS

    collaborator INDUSTRY

  • Mammograaf Radioloogiakliinik

    lead OTHER

Principal Investigators

  • Marina Astapova, MD · Mammograaf Radioloogiakliinik

Eligibility

Min Age
30 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2026-03-15
Completion
2026-09-30

Countries

  • Estonia

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07556900 on ClinicalTrials.gov