Breast cancer Cancer
Conditions
Interventions
This is a single-site prospective feasibility study comparing double reading with Mia (AI software as a medical device) with standard double reading in a local breast screening programme. The study wi
Sponsors
Kheiron Medical Technologies (United Kingdom)
Eligibility
Sex/Gender
Female
Age
49 Years to 70 Years
Inclusion criteria
Inclusion criteria: 1. Participants routinely invited or self-referred for breast screening 2. Participants identified as female 3. Participants for whom a 2D FFDM standard four-view mammography examination was acquired
Exclusion criteria
Exclusion criteria: 1. Patients with cosmetic breast implants 2. Very high risk patients, including genetic abnormalities etc. 3. Patients who have actively dissented from participation in the study 4. Cases marked as technical recall
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The absolute and relative differences in consensus between arbitration and Mia will be compared between Arm A and Arm B using readers' opinions at the end of recruitment | — |
Secondary
| Measure | Time frame |
|---|---|
| All secondary outcome measures will be measured at the end of recruitment. Measurements including consensus, discordance rate, recall rate, arbitration rate, and proportion of cases/cancers in different scenarios will use reader opinions to calculate. Measurements including cancer detection rate, sensitivity, specificity, and positive predictive value will use reader opinions and cancer follow-up information to calculate. 1. Clinical metrics including recall rate, cancer detection rate, sensitivity, specificity, and positive predictive value will be measured for each reader 2. Discordance rate between Mia and Reader 1 to quantify potential workload saving 3. Proportion of cases and cancers in different scenarios in Arm A 4. Consensus between arbitration and each reader per scenario and across all scenarios in Arm A 5. Recall rate and cancer detection rate of arbitration per scenario in Arm A 6. Potential to reduce recall rate and increase cancer detection rate in specific scenarios in Arm A 7. Measurement comparisons between Arm A and Arm B, measuring the proportion of cases and cancers in different scenarios and consensus between arbitration and each reader in different scenarios 8. Measurements and comparisons between simulated double reading in Arm A to Arm B, measuring recall rate, arbitration rate, cancer detection rate, sensitivity, specificity, and positive predictive value 9. Simulations of the 'extra reader' workflow, measuring recall rate, cancer detection rate, sensitivity, specificity, and positive predictive value, to understand potential clinical benefits | — |
Countries
England, United Kingdom
Contacts
Public ContactAnnie Ng
Outcome results
None listed