NCT05048095

Brief 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 mammography screening (double reading and consensus without AI).

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
15,500

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2021

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 8, 2021

Completed
9 days until next milestone

First Posted

Study publicly available on registry

September 17, 2021

Completed
28 days until next milestone

Study Start

First participant enrolled

October 15, 2021

Completed
4 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 15, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

February 15, 2022

Completed
Last Updated

April 20, 2022

Status Verified

April 1, 2022

Enrollment Period

4 months

First QC Date

September 8, 2021

Last Update Submit

April 19, 2022

Conditions

Breast Cancer

Keywords

Artificial intelligence

Outcome Measures

Primary Outcomes (3)

  • Cancer Detection rate

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

    After 4 months of inclusion

  • Recall or referral rate

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

    After 4 months of inclusion

  • Positive predictive value of referrals

    Proportion of women diagnosed with breast cancer among those referred

    After 4 months of inclusion

Secondary Outcomes (1)

  • Positive predictive value of Transpara® scores

    After 4 months of inclusion

Study Arms (1)

Screened women in Region Östergötland Linkoping

Other: AI cancer detection system

Interventions

AI cancer detection systemOTHER

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

Screened women in Region Östergötland Linkoping

Eligibility Criteria

Age40 Years - 74 Years
Sexfemale(Gender-based eligibility)
Gender Eligibility DetailsFemale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Women eligible for population-based mammography screening

You may qualify if:

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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Region Östergötland

Linköping, Östergötland County, 58185, Sweden

Location

Related Publications (10)

  • 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.

    PMID: 30834436BACKGROUND

  • 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.

    PMID: 30457482BACKGROUND

  • 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.

    PMID: 33948701BACKGROUND

  • 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.

    PMID: 34227882BACKGROUND

  • 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.

    PMID: 33944627BACKGROUND

  • 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.

    PMID: 32876835BACKGROUND

  • 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.

    PMID: 30993432BACKGROUND

  • 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.

    PMID: 33486604BACKGROUND

  • 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.

    PMID: 32052311BACKGROUND

  • 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.

    PMID: 34383147BACKGROUND

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Officials

  • Håkan Gustafsson, PhD

    Linköping University - University Hospital

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Adjunct Senior Lecturer

Study Record Dates

First Submitted

September 8, 2021

First Posted

September 17, 2021

Study Start

October 15, 2021

Primary Completion

February 15, 2022

Study Completion

February 15, 2022

Last Updated

April 20, 2022

Record last verified: 2022-04

Locations

SE(1)