The Use of AI to Safely Reduce the Workload in Breast Cancer Screening With Mammography in Region Östergötland (AIM-RÖ)

The Use of Artificial Intelligence (AI) to Safely Reduce the Workload in Breast Cancer Screening With Mammography in Region Östergötland

The overall aim of the project is to investigate how artificial intelligence (AI) can be used to streamline and at the same time increase diagnostic safety in breast cancer screening with mammography. AI has been shown in a number of studies to have great potential for both increasing diagnostic certainty (e.g. reduced occurrence of interval cancers) and at the same time reducing the workload for doctors. However, much research remains to clinically validate these new tools and to increase the understanding of how they affect the work of doctors. The specific goal of the project is to investigate whether the implementation of AI in breast cancer screening in Östergötland, Sweden, can increase the sensitivity (the mammography examination's ability to find breast cancer) and the specificity (that is, the right case is selected for further investigation: a minimum of healthy women are recalled but so many breast cancer cases that are possible are selected for further investigation) and at the same time make screening more efficient through reduced workload. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants.

Study Overview

• Status: Recruiting
• Conditions: Neoplasms; Breast Neoplasms; Breast Diseases; Artificial Intelligence
• Intervention / Treatment: Device: AI cancer detection system

Detailed Description

The overall aim of the project is to study whether the use of artificial intelligence can improve breast cancer screening with mammography. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants. The specific objective is to investigate whether the use of AI leads to increased diagnostic safety in mammography in Östergötland (measured as a reduced incidence of interval cancer) and at the same time leads to a reduced workload for the breast radiologists. Furthermore, the intention is to investigate how the use of AI affects the breast radiologists´ work in terms of reading time per examination and whether the radiologists' specificity and sensitivity are affected when they have access to the decision support based on AI during the review compared to if they do not have this support.

The hypotheses are that:

1. The use of AI in breast cancer screening in Östergötland Sweden improves the diagnostic quality. As a result, more breast cancer cases are detected early and the incidence of interval cancer decreases.
2. The reduced workload for the radiologists in Östergötland that could be demonstrated through the data collected in Östergötland 2021-2022 [NCT05048095 - Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping (AI-ROL)] can also be demonstrated in a large-scale prospective study.
3. Through the use of an AI-based decision support, not only can double review be eliminated for those cases where the AI assesses the cancer risk as low, but also each examination can be reviewed more quickly while maintaining or improving diagnostic certainty.
4. It is the least experienced radiologists who are most helped by the decision support, both for increased diagnostic certainty and increased efficiency.

• Study Type: Observational
• Enrollment (Estimated): 60000

Contacts and Locations

• Study Contact: Name: Håkan Gustafsson, Ph.D.; Phone Number: +46101043023; Email: hakan.l.gustafsson@liu.se

Study Locations

• Sweden
  • Östergötland
    • Linköping, Östergötland, Sweden
      • Recruiting
      • Region Östergötland
      • Contact: Håkan Gustafsson, Ph.D.; Phone Number: +46709428446; Email: hakan.l.gustafsson@liu.se

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Women eligible for population-based mammography screening

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Screened women in Region Östergötland, Sweden; All screened women in Region Östergötland, Sweden.

Device: AI cancer detection system; The AI system Transpara (Screenpoint Medical, The Netherlands) will be implemented for triaging two-image mammography examinations based on the probability of malignancy. Transpara assigns a score from 1 to 10 to each examination, indicating the risk of malignancy. A score between 1 and 7 indicates a low risk of cancer, 8-9 indicates an intermediate and 10 an elevated risk of cancer. Examinations with an AI score between 1 and 7 will be reviewed by only one radiologist, while examinations with an AI score > 7 will be double-reviewed as normal.; Other Names: Screenpoint Transpara

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Cancer Detection rate	Proportion of women diagnosed with breast cancer among those recalled after consensus	4 Years
Positive predictive value of Transpara® scores	Proportion of breast cancers diagnosed among women with a given AI score	4 Years

Collaborators and Investigators

• Sponsor: Ostergotland County Council, Sweden
• Investigators: Principal Investigator: Håkan Gustafsson, Ph.D., Region Östergötland

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2023
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: July 10, 2023
• First Submitted That Met QC Criteria: December 29, 2023
• First Posted (Estimated): January 1, 2024

Study Record Updates

• Last Update Posted (Estimated): January 1, 2024
• Last Update Submitted That Met QC Criteria: December 29, 2023
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms by Site; Neoplasms; Breast Neoplasms; Breast Diseases
• Other Study ID Numbers: AIM-RÖ

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: No individual participant data (IPD) will be shared.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No