Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping

Breast Cancer Clinical Trial

— AI-ROL  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05048095
• Other study ID #: NCT20210157-AI-ROL
• Status: Completed
• Start date: October 15, 2021
• Est. completion date: February 15, 2022

Study information

• Verified date: April 2022
• Source: Ostergotland County Council, Sweden
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 15500
• Est. completion date: February 15, 2022
• Est. primary completion date: February 15, 2022
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 40 Years to 74 Years

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

• Women with breast implants or other foreign implants in the mammogram

• Women with symptoms or signs of suspected breast cancer

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Other:
• AI cancer detection system
The use of AI as a third reader and as a decision support system during consensus meeting

Locations

Country	Name	City	State
Sweden	Region Östergötland	Linköping	Östergötland

Sponsors (1)

Lead Sponsor	Collaborator
Ostergotland County Council, Sweden

Country where clinical trial is conducted

Sweden, 

References & Publications (10)

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. — View Citation

Lång K, Dustler M, Dahlblom V, Åkesson 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. — View Citation

Lång K, Hofvind S, Rodríguez-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. — View Citation

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. — View Citation

Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-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. — View Citation

Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner 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. — View Citation

Rodriguez-Ruiz A, Lång 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. — View Citation

Rodriguez-Ruiz A, Lång 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. — View Citation

Sasaki M, Tozaki M, Rodríguez-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. — View Citation

van Winkel SL, Rodríguez-Ruiz A, Appelman L, Gubern-Mérida 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Cancer Detection rate	Proportion of women diagnosed with breast cancer among those recalled after consensus	After 4 months of inclusion
Primary	Recall or referral rate	Proportion of women who are referred for further diagnostic workup after consensus	After 4 months of inclusion
Primary	Positive predictive value of referrals	Proportion of women diagnosed with breast cancer among those referred	After 4 months of inclusion
Secondary	Positive predictive value of Transpara® scores	Proportion of breast cancers diagnosed among women with a given AI score	After 4 months of inclusion