Artificial Intelligence in Breast Cancer Screening Programs in Córdoba (AITIC)

Breast Cancer Clinical Trial

— AITIC  

Official title:

New Strategies Based on Artificial Intelligence in Breast Cancer Screening Programs in Córdoba With Digital Mammography and Digital Breast Tomosynthesis. A Prospective Evaluation.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04949776
• Other study ID #: AITIC
• Status: Recruiting
• Phase: N/A
• Start date: March 1, 2022
• Est. completion date: February 2024

Study information

• Verified date: August 2023
• Source: Maimónides Biomedical Research Institute of Córdoba
• Contact: Esperanza Elias Cabot, MD
• Phone: 0034957213700
• Email: eeliascabot[@]gmail.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 27000
• Est. completion date: February 2024
• Est. primary completion date: February 2024
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 50 Years to 69 Years

Eligibility

Inclusion Criteria:

• Any woman between the ages of 50 and 69, from the hospital area of the Reina Sofía University Hospital, invited to the Breast Cancer Early Detection Program, summoned in rooms 2024 and 2001 and who agrees to participate in the study by signing the consent informed.

• Women studied in the program in the set period and who have previously participated.

• Women who are studied in the program for the first time in the set period.

Exclusion Criteria:

• Women invited to the program who do not agree to enter the research study by signing the informed consent.

• Women with breast prostheses.

• Women with symptoms or signs of suspected breast cancer.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Diagnostic Test:
• Mammograms
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®.

Locations

Country	Name	City	State
Spain	Hospital Universitario Reina Sofia	Córdoba

Sponsors (1)

Lead Sponsor	Collaborator
Maimónides Biomedical Research Institute of Córdoba

Country where clinical trial is conducted

Spain, 

References & Publications (7)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.
Primary	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.
Primary	Detection rate	Proportion of women diagnosed with breast cancer among those screened.	In the middle of the study, at 1 year.
Primary	Detection rate	Proportion of women diagnosed with breast cancer among those screened.	At the end of the study, at 2 years.
Primary	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.
Primary	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	Positive predictive value of referrals	Proportion of women diagnosed with breast cancer among those referred to the hospital.	In the middle of the study, at 1 year.
Secondary	Positive predictive value of referrals	Proportion of women diagnosed with breast cancer among those referred to the hospital.	At the end of the study, at 2 years.
Secondary	Positive predictive value of biopsies	Proportion of women with breast cancer among all women undergoing biopsy.	In the middle of the study, at 1 year.
Secondary	Positive predictive value of biopsies	Proportion of women with breast cancer among all women undergoing biopsy.	At the end of the study, at 2 years.
Secondary	Positive predictive value of Transpara® scores	Proportion of breast cancers diagnosed among women with a given score.	In the middle of the study, at 1 year.
Secondary	Positive predictive value of Transpara® scores	Proportion of breast cancers diagnosed among women with a given score.	At the end of the study, at 2 years.