Decision support in breast cancer screening Cancer
Conditions
Interventions
The intervention is the sponsor's deep learning software (Mia), assessed on de-identified randomised retrospective breast screening cases and outcomes. Comparison is made against the control arm of ex
Sponsors
Kheiron Medical Technologies (United Kingdom)
Eligibility
Sex/Gender
Female
Inclusion criteria
Inclusion criteria: 1. Female participants 2. Participants attending for breast screening purposes (normal and opportunistic screening)* 3. Participants for whom a 2D FFDM standard four-view mammography examination was acquired (MLO-R, CC-R, MLO-L, CC-L) * Includes: 1) early recall cases (e.g. participants brought back for screening earlier than the established screening interval); and 2) participants of UK age extension trial (AgeX).
Exclusion criteria
Exclusion criteria: Does not meet inclusion criteria
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity of the standalone case-wise malignancy detection performance of Mia, measured as the number of positive cases recalled divided by the total number of positive cases, over the full study dataset time period. Specificity of the standalone case-wise malignancy detection performance of Mia, measured as the number of confirmed negative cases not recalled divided by the total number of confirmed negative cases, over the full dataset time period. | — |
Secondary
| Measure | Time frame |
|---|---|
| Current secondary outcome measures as if 17/07/2023: 1. Measurement of relevant clinical metrics for Mia standalone and descriptives of cancer subtypes that Mia picks up to understand Mia’s contribution to double reading. 2. Comparison of Mia standalone and the historical first reader to understand differences in how their performance contributes to double reading. 3. Measurement of relevant clinical metrics, including resource/workload savings, and descriptives of cancer subtypes picked up in double reading workflows that incorporate Mia as an independent reader, to understand the performance of each workflow and to inform future health economic assessments. 4. Comparison of double reading workflows with and without Mia on clinically relevant metrics to understand differences in performance. 5. Comparison of double reading workflows with Mia against UK national guidelines to assess if guidelines thresholds would be met if Mia was used in double reading. 6. Secondary outcomes will also include performance stratified by region/site and ethnicity to confirm generalisability. _____ Previous secondary outcome measures: 1. Recall rate, negative flag rate, cancer detection rate, sensitivity, specificity, interval cancer rate, positive predictive value, percentage of interval cancers recalled, area under the receiver operating characteristic curve will be measured for Mia’s standalone performance over the study dataset time period and a selected one year period with the most complete data 2. Recall rate, cancer detection rate, sensitivity, specificity, interval cancer rate, positive predictive value, arbitration rate will be measured for various simulated double reading workflows with Mia over the study dataset time period and a selected one year period with the most complete data 3. Non-inferiority and superiority and associated absolute and relative differences between Mia standalone against the historical first reader will be measured for cancer detection rate, s | — |
Countries
England, United Kingdom
Outcome results
None listed