Breast CancerAITICNCT04949776
Artificial Intelligence in Breast Cancer Screening Programs
A study testing Mammograms in people with Breast Cancer and Screening, run by Maimónides Biomedical Research Institute of Córdoba at 1 site in Spain.
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… Read more about this study
- Who is it for?
- Ages 50 to 71 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer, Screening
- What is being tested?
- Mammograms
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 31,301 participants
- Where
- 1 siteSpain
- Study dates
- 15 Mar 2022to 11 Jan 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 71 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
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.
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
- Being tested
- Mammograms
- Keywords
- breast cancer, artificial intelligence, Digital breast tomosynthesis, Mammography, Mass screening
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MammogramsDiagnostic 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®.
| Study group | Role | What they receive |
|---|---|---|
| Double reading of all cases with and without Transpara software | Experimental | Double reading of all cases with and without Transpara software Receives: Diagnostic Test: Mammograms |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: At the end of the study, at 2 years.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Measured: In the middle of the study, at 1 year.
Detection rate
Proportion of women diagnosed with breast cancer among those screened.
Measured: 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.
Measured: 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.
Measured: At the end of the study, at 2 years.
Secondary outcomes (6)
Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Measured: In the middle of the study, at 1 year.
Positive predictive value of referrals
Proportion of women diagnosed with breast cancer among those referred to the hospital.
Measured: At the end of the study, at 2 years.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Measured: In the middle of the study, at 1 year.
Positive predictive value of biopsies
Proportion of women with breast cancer among all women undergoing biopsy.
Measured: At the end of the study, at 2 years.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.
Measured: In the middle of the study, at 1 year.
Positive predictive value of Transpara® scores
Proportion of breast cancers diagnosed among women with a given score.
Measured: At the end of the study, at 2 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jul 2021
- Study start15 Mar 2022
- Primary completion11 Jan 2024
- Study completion11 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 31,301 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maimónides Biomedical Research Institute of Córdoba
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Esperanza Elias Cabot, MD, Principal investigator, Hospital Universitario Reina Sofia de Cordoba
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04949776
- Sponsor's protocol ID
- AITIC
- Acronym
- AITIC
- Status last verified
- Aug 2025
- First submitted
- 15 Jun 2021
- Last updated
- 14 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The database and the protocol Will be shared after the trial is published.
Publications
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.
PubMed 33944627Rodriguez-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.
PubMed 30834436Rodriguez-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.
PubMed 30993432Rodriguez-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.
PubMed 30457482Yala 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.
PubMed 31385754Sasaki 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.
PubMed 32052311Le 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.
PubMed 30898381
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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.
- ClinicalTrials.gov ID
- NCT04949776
- Acronym / sponsor ID
- AITIC / AITIC
- Conditions
- Breast Cancer, Screening
- Keywords
- breast cancer, artificial intelligence, Digital breast tomosynthesis, Mammography, Mass screening
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 31,301 participants (actual)
- Lead sponsor
- Maimónides Biomedical Research Institute of CórdobaUniversity, hospital or other
- Study start
- 15 Mar 2022 (actual)
- Primary completion
- 11 Jan 2024 (actual)
- Study completion
- 11 Jan 2024 (actual)
- First posted
- 2 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Double reading of all cases with and without Transpara software | Experimental | Diagnostic Test: MammogramsDouble reading of all cases with and without Transpara software |
Who may qualify
- Ages
- 50 to 71 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MammogramsIn 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®. | Diagnostic test | Double reading of all cases with and without Transpara software |
Primary outcome
Assessment of Workload of each strategy
Time frame: In the middle of the study, at 1 year.
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
Time frame: At the end of the study, at 2 years.
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
Time frame: In the middle of the study, at 1 year.
Proportion of women diagnosed with breast cancer among those screened.
Detection rate
Time frame: At the end of the study, at 2 years.
Proportion of women diagnosed with breast cancer among those screened.
Recall or referral rate
Time frame: In the middle of the study, at 1 year.
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
Recall or referral rate
Time frame: At the end of the study, at 2 years.
Proportion of women who, after the screening test, are referred to the breast diagnosis unit.
6 secondary outcomes
Positive predictive value of referrals
Time frame: In the middle of the study, at 1 year.
Positive predictive value of referrals
Time frame: At the end of the study, at 2 years.
Positive predictive value of biopsies
Time frame: In the middle of the study, at 1 year.
Positive predictive value of biopsies
Time frame: At the end of the study, at 2 years.
Positive predictive value of Transpara® scores
Time frame: In the middle of the study, at 1 year.
Positive predictive value of Transpara® scores
Time frame: At the end of the study, at 2 years.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2025.
- Background: PubMed 33944627Raya-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.
- Background: PubMed 30834436Rodriguez-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.
- Background: PubMed 30993432Rodriguez-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.
- Background: PubMed 30457482Rodriguez-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.
- Background: PubMed 31385754Yala 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.
- Background: PubMed 32052311Sasaki 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.
- Background: PubMed 30898381Le 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.
- Individual participant dataSharing planned: Yes. The database and the protocol Will be shared after the trial is published.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Esperanza Elias Cabot, MD principal investigator
Hospital Universitario Reina Sofia de Cordoba
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Mammograms in people with Breast Cancer and Screening, run by Maimónides Biomedical Research Institute of Córdoba at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 14 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Assessment of Workload of each strategy (time frame: In the middle of the study, at 1 year.). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04949776, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Aug 2025.