Artificial Intelligence in Breast Cancer Screening Programs

NCT04949776 | Completed

• 1 other identifier: AITIC
• Study Type: interventional
• Target: 31,301
• Locations: 1 country
• Sites: 1

Brief Summary

The use of artificial intelligence software in breast screening (Transpara®) makes it possible to identify studies with a very low probability of cancer. The hypothesis raised in this work is that reading strategies based on artificial intelligence (single or double reading only of cases with a score\> 7 with Transpara®), allow reducing the workload of a screening program by more than 50 % with respect to the standard reading of the program (double reading of all cases without Transpara®), without presenting inferiority in terms of detection rates and recalls of the program, both with the use of 2D digital mammography and with the use of tomosynthesis or 3D mammogram.

Conditions

Breast Cancer; Screening

Keywords

breast cancer; artificial intelligence; Digital breast tomosynthesis; Mammography; Mass screening

Outcome Measures

Primary Outcomes (6)

• Assessment of Workload of each strategy

  The workload of each strategy shall be assessed by multiplying the average time for a reading of that strategy by the total number of readings of that strategy. The average reading time of a case in each strategy shall be calculated from the measurement of the individual reading time in a sample of 500 cases in each strategy.

  In the middle of the study, at 1 year.

• Assessment of Workload of each strategy

  The workload of each strategy shall be assessed by multiplying the average time for a reading of that strategy by the total number of readings of that strategy. The average reading time of a case in each strategy shall be calculated from the measurement of the individual reading time in a sample of 500 cases in each strategy.

  At the end of the study, at 2 years.

• Detection rate

  Proportion of women diagnosed with breast cancer among those screened.

  In the middle of the study, at 1 year.

• Detection rate

  Proportion of women diagnosed with breast cancer among those screened.

  At the end of the study, at 2 years.

• Recall or referral rate

  Proportion of women who, after the screening test, are referred to the breast diagnosis unit.

  In the middle of the study, at 1 year.

• Recall or referral rate

  Proportion of women who, after the screening test, are referred to the breast diagnosis unit.

  At the end of the study, at 2 years.

Secondary Outcomes (6)

• Positive predictive value of referrals

  In the middle of the study, at 1 year.

• Positive predictive value of referrals

  At the end of the study, at 2 years.

• Positive predictive value of biopsies

  In the middle of the study, at 1 year.

• Positive predictive value of biopsies

  At the end of the study, at 2 years.

• Positive predictive value of Transpara® scores

  In the middle of the study, at 1 year.

Study Arms (1)

Double reading of all cases with and without Transpara software

EXPERIMENTAL

Double reading of all cases with and without Transpara software

Diagnostic Test: Mammograms

Interventions

Mammograms DIAGNOSTIC_TEST

In the women participating in the study, two strategies for reading mammograms will be carried out: Strategy 1: Standard reading of the program. Double independent and non-consensual reading of all cases, without any artificial intelligence system (standard strategy). Strategy 2: Reading strategy based on the global Score granted by Transpara® (strategy based on artificial intelligence): * In studies with a Score \<8 (studies with a low probability of cancer): They will not be evaluated by any radiologist. * In studies with a Score\> 7 (studies with a high probability of cancer): double reading will be carried out, assisted by Transpara®.

Double reading of all cases with and without Transpara software

Eligibility Criteria

• Age: 50 Years - 71 Years
• Gender Eligibility Details: Women participating in the regular Breast Cancer Early Detection Program in Cordoba
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• All women between 50 and 71 years of age (including women who reach that age in the year of appointment), in the Reina Sofía University Hospital district, invited to participate in the Breast Cancer Early Detection Program, that have been randomly assigned in the Hologic equipment (DM or DBT), and who agree to participate in the study by signing the informed consent form.
• Women studied in the program during the established period and who have previously participated.
• Women studied in the program for the first time in the established period.

You may not qualify if:

• Women invited to the program who do not agree to participate in the research study by signing the informed consent form.
• Women with breast prostheses.
• Women with signs or symptoms of suspected breast cancer.

Sponsors & Collaborators

• Maimónides Biomedical Research Institute of Córdoba: lead

Study Sites (1)

Hospital Universitario Reina Sofia

Córdoba, Córdoba, 14004, Spain

Location

Related Publications (7)

• Raya-Povedano JL, Romero-Martin S, Elias-Cabot E, Gubern-Merida A, Rodriguez-Ruiz A, Alvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021 Jul;300(1):57-65. doi: 10.1148/radiol.2021203555. Epub 2021 May 4.

  PMID: 33944627 BACKGROUND

• Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916-922. doi: 10.1093/jnci/djy222.

  PMID: 30834436 BACKGROUND

• Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Teuwen J, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Sechopoulos I, Mann RM. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol. 2019 Sep;29(9):4825-4832. doi: 10.1007/s00330-019-06186-9. Epub 2019 Apr 16.

  PMID: 30993432 BACKGROUND

• Rodriguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Kobrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371. Epub 2018 Nov 20.

  PMID: 30457482 BACKGROUND

• Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology. 2019 Oct;293(1):38-46. doi: 10.1148/radiol.2019182908. Epub 2019 Aug 6.

  PMID: 31385754 BACKGROUND

• Sasaki M, Tozaki M, Rodriguez-Ruiz A, Yotsumoto D, Ichiki Y, Terawaki A, Oosako S, Sagara Y, Sagara Y. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer. 2020 Jul;27(4):642-651. doi: 10.1007/s12282-020-01061-8. Epub 2020 Feb 12.

  PMID: 32052311 BACKGROUND

• Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol. 2019 May;74(5):357-366. doi: 10.1016/j.crad.2019.02.006. Epub 2019 Mar 18.

  PMID: 30898381 BACKGROUND

MeSH Terms

Conditions

Breast Neoplasms

Interventions

Mammography

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Radiography; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Esperanza Elias Cabot, MD

  Hospital Universitario Reina Sofia de Cordoba

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: June 15, 2021
• First Posted: July 2, 2021
• Study Start: March 15, 2022
• Primary Completion: January 11, 2024
• Study Completion: January 11, 2024
• Last Updated: August 14, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF, CSR
• Time Frame: After the trial is published.
• Access Criteria: Upon request to the principal investigator.

Locations

ES (1)