Clinical trial NCT04949776: Artificial Intelligence in Breast Cancer Screening Programs in Córdoba (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.
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Breast Cancer
Keywords: breast cancer
artificial intelligence
Lead sponsor: Maimónides Biomedical Research Institute of Córdoba
Sponsor class: OTHER
First posted: 2021-07-02
Enrollment: 27000
Sex: FEMALE
Minimum age (years): 50
Maximum age (years): 69
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Assessment of Workload of each strategy
timeFrame: In the middle of the study, at 1 year.
measure: Assessment of Workload of each strategy
timeFrame: At the end of the study, at 2 years.
measure: Detection rate
timeFrame: In the middle of the study, at 1 year.
measure: Detection rate
timeFrame: At the end of the study, at 2 years.
measure: Recall or referral rate
timeFrame: In the middle of the study, at 1 year.
measure: Recall or referral rate
timeFrame: At the end of the study, at 2 years.
Secondary outcomes:
measure: Positive predictive value of referrals
timeFrame: In the middle of the study, at 1 year.
measure: Positive predictive value of referrals
timeFrame: At the end of the study, at 2 years.
measure: Positive predictive value of biopsies
timeFrame: In the middle of the study, at 1 year.
measure: Positive predictive value of biopsies
timeFrame: At the end of the study, at 2 years.
measure: Positive predictive value of Transpara® scores
timeFrame: In the middle of the study, at 1 year.
measure: Positive predictive value of Transpara® scores
timeFrame: At the end of the study, at 2 years.
Other outcomes:
Locations:
city: Córdoba
country: Spain
facility: Hospital Universitario Reina Sofia
Study officials:
affiliation: Hospital Universitario Reina Sofia de Cordoba
name: Esperanza Elias Cabot, MD
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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.
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.
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.
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.
citation: 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.