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

Primary Purpose

Breast Cancer

Status
Recruiting
Phase
Not Applicable
Locations
Spain
Study Type
Interventional
Intervention
Mammograms
Sponsored by
Maimónides Biomedical Research Institute of Córdoba
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Breast Cancer focused on measuring breast cancer, artificial intelligence

Eligibility Criteria

50 Years - 69 Years (Adult, Older Adult)FemaleDoes not accept healthy volunteers

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.

Sites / Locations

  • Hospital Universitario Reina SofiaRecruiting

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Double reading of all cases with and without Transpara software

Arm Description

Double reading of all cases with and without Transpara software

Outcomes

Primary Outcome Measures

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.
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.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Recall or referral rate
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Recall or referral rate
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.

Secondary Outcome Measures

Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.

Full Information

First Posted
June 15, 2021
Last Updated
August 24, 2023
Sponsor
Maimónides Biomedical Research Institute of Córdoba
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1. Study Identification

Unique Protocol Identification Number
NCT04949776
Brief Title
Artificial Intelligence in Breast Cancer Screening Programs in Córdoba (AITIC)
Acronym
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

2. Study Status

Record Verification Date
August 2023
Overall Recruitment Status
Recruiting
Study Start Date
March 1, 2022 (Actual)
Primary Completion Date
February 2024 (Anticipated)
Study Completion Date
February 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Maimónides Biomedical Research Institute of Córdoba

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Breast Cancer
Keywords
breast cancer, artificial intelligence

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
27000 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Double reading of all cases with and without Transpara software
Arm Type
Experimental
Arm Description
Double reading of all cases with and without Transpara software
Intervention Type
Diagnostic Test
Intervention Name(s)
Mammograms
Intervention Description
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®.
Primary Outcome Measure Information:
Title
Assessment of Workload of each strategy
Description
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.
Time Frame
In the middle of the study, at 1 year.
Title
Assessment of Workload of each strategy
Description
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.
Time Frame
At the end of the study, at 2 years.
Title
Detection rate
Description
Proportion of women diagnosed with breast cancer among those screened.
Time Frame
In the middle of the study, at 1 year.
Title
Detection rate
Description
Proportion of women diagnosed with breast cancer among those screened.
Time Frame
At the end of the study, at 2 years.
Title
Recall or referral rate
Description
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Time Frame
In the middle of the study, at 1 year.
Title
Recall or referral rate
Description
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Time Frame
At the end of the study, at 2 years.
Secondary Outcome Measure Information:
Title
Positive predictive value of referrals
Description
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Time Frame
In the middle of the study, at 1 year.
Title
Positive predictive value of referrals
Description
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Time Frame
At the end of the study, at 2 years.
Title
Positive predictive value of biopsies
Description
Proportion of women with breast cancer among all women undergoing biopsy.
Time Frame
In the middle of the study, at 1 year.
Title
Positive predictive value of biopsies
Description
Proportion of women with breast cancer among all women undergoing biopsy.
Time Frame
At the end of the study, at 2 years.
Title
Positive predictive value of Transpara® scores
Description
Proportion of breast cancers diagnosed among women with a given score.
Time Frame
In the middle of the study, at 1 year.
Title
Positive predictive value of Transpara® scores
Description
Proportion of breast cancers diagnosed among women with a given score.
Time Frame
At the end of the study, at 2 years.

10. Eligibility

Sex
Female
Gender Based
Yes
Gender Eligibility Description
Women participating in the regular Breast Cancer Early Detection Program in Cordoba
Minimum Age & Unit of Time
50 Years
Maximum Age & Unit of Time
69 Years
Accepts Healthy Volunteers
No
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.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Esperanza Elias Cabot, MD
Phone
0034957213700
Email
eeliascabot@gmail.com
First Name & Middle Initial & Last Name or Official Title & Degree
Cristina Amate
Phone
0034957213700
Email
cristina.amate@imibic.org
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Esperanza Elias Cabot, MD
Organizational Affiliation
Hospital Universitario Reina Sofia de Cordoba
Official's Role
Principal Investigator
Facility Information:
Facility Name
Hospital Universitario Reina Sofia
City
Córdoba
ZIP/Postal Code
14004
Country
Spain
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Esperanza Elias Cabot, MD
Email
eeliascabot@gmail.com
First Name & Middle Initial & Last Name & Degree
Cristina Amate
Email
cristina.amate@imibic.org
First Name & Middle Initial & Last Name & Degree
Esperanza Elias Cabot, MD

12. IPD Sharing Statement

Plan to Share IPD
Yes
IPD Sharing Plan Description
The database and the protocol Will be shared after the trial is published.
IPD Sharing Time Frame
After the trial is published.
IPD Sharing Access Criteria
Upon request to the principal investigator.
Citations:
PubMed Identifier
33944627
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.
Results Reference
background
PubMed Identifier
30834436
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.
Results Reference
background
PubMed Identifier
30993432
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.
Results Reference
background
PubMed Identifier
30457482
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.
Results Reference
background
PubMed Identifier
31385754
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.
Results Reference
background
PubMed Identifier
32052311
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.
Results Reference
background
PubMed Identifier
30898381
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.
Results Reference
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Artificial Intelligence in Breast Cancer Screening Programs in Córdoba (AITIC)

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