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Breast CancerAI-ROLNCT05048095

Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping

An observational study of Breast Cancer, run by Ostergotland County Council, Sweden at 1 site in Sweden.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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Trial summary

The purpose of this observational study is to assess whether the use of AI (Transpara®) can lead to an improved quality of a double reading mammography screening program. This is investigated by performing AI as a third reader and as a decision support during the consensus meeting, compared with conventional… Read more about this study

Who is it for?
Ages 40 to 74 years, women only
Conditions the study is for
Breast Cancer
What is being tested?
AI cancer detection system
How many people
15,500 participants
Where
1 siteSweden
Study dates
15 Oct 2021to 15 Feb 2022
Phase
Observational
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 40 to 74 years
  • Women only
  • No healthy volunteers
  • Adult, Older adult

Study population: Women eligible for population-based mammography screening

Who may be able to join

  • Women participating in the regular Breast Cancer Screening Program in Region Östergötland Linkoping

Who cannot join

  • Women with breast implants or other foreign implants in the mammogram
  • Women with symptoms or signs of suspected breast cancer

Trial locations and contacts

1 site in 1 country.

Sweden1 site

  • Region Östergötland

    Linköping, Östergötland County, 58185

Other trials in Sweden

About this study

Official title: The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden

The purpose of this observational study is to assess whether the use of AI (Transpara®) can lead to an improved quality of a double reading mammography screening program. This is investigated by performing AI as a third reader and as a decision support during the consensus meeting, compared with conventional mammography screening (double reading and consensus without AI).

Read the detailed description

The AI cancer detection system will act as a 3rd reader and will recall additional cases to the consensus conference: the exams that were not recalled by double reading but are classified as the 3% most suspicious exams, based on AI derived cancer-risk scores. Secondly, AI is used as a decision support during consensus. AI risk scores and Computer-Aided Detection (CAD)-marks of suspicious calcifications and soft tissue lesions are provided to the reader(s).

The hypothesis of this study is that the use of AI has the potential to improve the quality of the screening program by increasing the cancer detection rate without affecting the recall rate.

Conditions
Breast Cancer
Being tested
AI cancer detection system
Keywords
Artificial intelligence
Medical subject headings
Breast Neoplasms

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • AI cancer detection systemOther

    The use of AI as a third reader and as a decision support system during consensus meeting

Study groups in NCT05048095
Study groupRoleWhat they receive
Screened women in Region Östergötland LinkopingNot described
Receives: Other: AI cancer detection system

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Cancer Detection rate

    Proportion of women diagnosed with breast cancer among those recalled after consensus

    Measured: After 4 months of inclusion

  2. Recall or referral rate

    Proportion of women who are referred for further diagnostic workup after consensus

    Measured: After 4 months of inclusion

  3. Positive predictive value of referrals

    Proportion of women diagnosed with breast cancer among those referred

    Measured: After 4 months of inclusion

Secondary outcomes (1)

  1. Positive predictive value of Transpara® scores

    Proportion of breast cancers diagnosed among women with a given AI score

    Measured: After 4 months of inclusion

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted17 Sep 2021
  2. Study start15 Oct 2021
  3. Primary completion15 Feb 2022
  4. Study completion15 Feb 2022

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
15,500 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05048095
Sponsor's protocol ID
NCT20210157-AI-ROL
Acronym
AI-ROL
Status last verified
Apr 2022
First submitted
8 Sep 2021
Last updated
20 Apr 2022
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • 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.

    PubMed 30834436

  • 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.

    PubMed 30457482

  • van Winkel SL, Rodriguez-Ruiz A, Appelman L, Gubern-Merida A, Karssemeijer N, Teuwen J, Wanders AJT, Sechopoulos I, Mann RM. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021 Nov;31(11):8682-8691. doi: 10.1007/s00330-021-07992-w. Epub 2021 May 4.

    PubMed 33948701

  • Pinto MC, Rodriguez-Ruiz A, Pedersen K, Hofvind S, Wicklein J, Kappler S, Mann RM, Sechopoulos I. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis. Radiology. 2021 Sep;300(3):529-536. doi: 10.1148/radiol.2021204432. Epub 2021 Jul 6.

    PubMed 34227882

  • 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 33944627

  • Lang K, Dustler M, Dahlblom V, Akesson A, Andersson I, Zackrisson S. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol. 2021 Mar;31(3):1687-1692. doi: 10.1007/s00330-020-07165-1. Epub 2020 Sep 2.

    PubMed 32876835

  • 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.

    PubMed 30993432

  • Lang K, Hofvind S, Rodriguez-Ruiz A, Andersson I. Can artificial intelligence reduce the interval cancer rate in mammography screening? Eur Radiol. 2021 Aug;31(8):5940-5947. doi: 10.1007/s00330-021-07686-3. Epub 2021 Jan 23.

    PubMed 33486604

  • 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.

    PubMed 32052311

  • Kerschke L, Weigel S, Rodriguez-Ruiz A, Karssemeijer N, Heindel W. Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance. Eur Radiol. 2022 Feb;32(2):842-852. doi: 10.1007/s00330-021-08217-w. Epub 2021 Aug 12.

    PubMed 34383147

Questions about this trial

What is this clinical trial studying?

An observational study of Breast Cancer, run by Ostergotland County Council, Sweden at 1 site in Sweden.

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 1 inclusion and 2 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 20 Apr 2022. 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 Sweden. 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?

Cancer Detection rate (time frame: After 4 months of inclusion). The study lists 3 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 NCT05048095, which also lists 10 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 20 Apr 2022; AzTrials last refreshed its copy on 6 Nov 2026.

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