Clinical trial NCT05048095: Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping
Official title: The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Breast Cancer
Keywords: Artificial intelligence
Lead sponsor: Ostergotland County Council, Sweden
Sponsor class: OTHER
First posted: 2021-09-17
Enrollment: 15500
Sex: FEMALE
Minimum age (years): 40
Maximum age (years): 74
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Women eligible for population-based mammography screening

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).

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.

Eligibility criteria: 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

Primary outcomes:
measure: Cancer Detection rate
timeFrame: After 4 months of inclusion
measure: Recall or referral rate
timeFrame: After 4 months of inclusion
measure: Positive predictive value of referrals
timeFrame: After 4 months of inclusion
Secondary outcomes:
measure: Positive predictive value of Transpara® scores
timeFrame: After 4 months of inclusion
Other outcomes:
Locations:
city: Linköping
country: Sweden
facility: Region Östergötland
state: Östergötland
Study officials:
affiliation: Linköping University - University Hospital
name: Håkan Gustafsson, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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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.
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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.
citation: 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.