Artificial intelligence in mammography study
| ISRCTN | ISRCTN60839016 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN60839016 |
| IRAS number | 303782 |
| Secondary identifying numbers | 21SM7312, IRAS 303782 |
- Submission date
- 25/05/2022
- Registration date
- 06/06/2022
- Last edited
- 06/06/2022
- Recruitment status
- No longer recruiting
- Overall study status
- Completed
- Condition category
- Cancer
Plain English Summary
Background and study aims
1 in 8 women will be diagnosed with breast cancer during their lifetime. It is a leading cause of cancer-related deaths among women worldwide. There is a need for rigorous large-scale studies to assess the performance of artificial intelligence (AI) for the diagnosis of breast cancer from breast scans (mammography). This should be done on diverse cohorts of women across multiple screening sites and on unenriched data representative of a true screening population. The aim of this study is to evaluate the performance of an AI system in detecting breast cancer on data representative of a true screening population.
Who can participate?
Being a retrospective study, no participants are directly involved in the study. There will be no effect or change to any participant’s care.
Retrospective study (Part A): Historical data from women 50-70 years old who had a mammography as part of the national breast screening programme. The study will evaluate the AI system based on its analysis of historical, de-identified cases from study sites.
Arbitration study (Part B): Participants in the reader study will be voluntarily recruited from participating sites. Readers must be either breast screening radiologists or film reading radiographers.
What does the study involve?
Data will be collected from a mammography image database with patient consent. There will be no impact on patient care. The intervention is the AI system, assessed on de-identified retrospective breast screening cases and outcomes. The study will look at how specialists interact with the AI system in the arbitration clinic.
What are the possible benefits and risks of participating?
The researchers do not anticipate any disadvantages or risks to taking part. They do not anticipate any immediate benefits from taking part in this study. However, the information from this study will help to assess if artificial intelligence has the potential to improve future clinical care in the UK breast screening programme and worldwide, by providing more accurate reads, improving breast cancer detection, and reducing the time to provide results to patients.
Where is the study run from?
Imperial College London (UK)
When is the study starting and how long is it expected to run for?
May 2021 to May 2024
Who is funding the study?
National Institute for Health Research (NIHR) (UK)
Who is the main contact?
Clinical Trial Manager, a.sy@imperial.ac.uk
Contact information
Principal Investigator
Institute of Global Health Innovation (IGHI) & Department of Surgery and Cancer
Imperial College London
10th Floor, Queen Elizabeth the Queen Mother Wing (QEQM)
St Mary's Campus
London
W2 1NY
United Kingdom
| Phone | +44 (0)2033121310 |
|---|---|
| a.darzi@imperial.ac.uk |
Study information
| Study design | Part A: retrospective diagnostic accuracy study; Part B: simulated usage of the AI system by readers in arbitration panels using retrospective data |
|---|---|
| Primary study design | Observational |
| Secondary study design | Database analysis, feasibility/pilot study and questionnaire, interview or observation study |
| Study setting(s) | Hospital |
| Study type | Screening |
| Participant information sheet | No participant information sheet available |
| Scientific title | Clinical validation of an artificial intelligence system to improve the quality, efficiency and experience of breast cancer screening |
| Study acronym | AIMS |
| Study hypothesis | A novel AI system for breast cancer screening demonstrates the appropriate accuracy, safety, acceptability, and cost-effectiveness required for use as an independent reader within the NHS breast screening programme. |
| Ethics approval(s) | Approved 14/02/2022, East Midlands - Nottingham 1 Research Ethics Committee (The Old Chapel, Royal Standard Place, Nottingham, NG1 6FS, UK; +44 (0)207 104 8115; Nottingham1.rec@hra.nhs.uk), ref: 22/EM/0038 |
| Condition | Decision support in breast cancer screening |
| Intervention | Data will be collected retrospectively from the OPTIMAM Mammography Image Database, with patient consent. There will be no impact on patient care. The intervention is the AI system, assessed on de-identified retrospective breast screening cases and outcomes. To understand how the AI system would perform within a "double reading'' screening workflow, the study will look at how specialists interact with the AI system in the arbitration clinic. |
| Intervention type | Other |
| Primary outcome measure | Sensitivity and specificity of AI system cancer detection measured as the number of positive cases (cases considered positive if they received a biopsy-confirmed diagnosis of cancer within 39 months following the screening visit. Negative cases will require a negative result from the study screening visit, and another negative result at the subsequent screening visit at least 31 months later) compared to first, second and consensus reader decisions. |
| Secondary outcome measures | 1. Case recall rate, cancer detection, positive predictive value, negative predictive value, cancer detection rate, area under the receiver operating characteristic curve will be measured for AI system performance over the study dataset time period 2. Subgroup performance by factors including cancer type and grade, primary tumour size, patient age, breast density, prior cancer, prevalent and incident screens, ethnicity, device manufacturer, socioeconomic status, and screening site over the study dataset time period 3. Analysis of failure cases for the study dataset time period 4. Percentage of women that meet the eligibility criteria over the course of the study 5. Simulations of workforce impact assessment and health economic modelling over the study period 6. AI system localisation performance (if lesion position data available) over the study period 7. AI system performance in confirmed interval cancers (percentage of historical interval cancers that the AI system flagged for recall, and qualitative agreement of the localisation in the original screening mammogram with the presence/absence of true radiological evidence) over the study period |
| Overall study start date | 20/05/2021 |
| Overall study end date | 21/05/2024 |
Eligibility
| Participant type(s) | Patient |
|---|---|
| Age group | Adult |
| Lower age limit | 50 Years |
| Upper age limit | 70 Years |
| Sex | Female |
| Target number of participants | Part A: 50,000; Part B: 18,000 arbitration cases |
| Participant inclusion criteria | 1. Women undergoing routine breast cancer screening (age 50-70 years) as part of the national breast screening programme from January 2016 onwards 2. Mammography images acquired using Hologic/Lorad, Siemens, or GE devices |
| Participant exclusion criteria | Part A: 1. Women attending an assessment clinic or symptomatic clinic (i.e. not routine screening) 2. Women undergoing annual screening due to: 2.1. High risk (lifetime risk >30% - e.g. faulty BRCA1, BRCA2, TP53) 2.2. Moderate risk (lifetime risk 17-30%) 2.3. Personal stratified follow up (e.g. indeterminate B3 lesions) 3. Presence of breast implants 4. Screens with incomplete (<4 standard screening views - e.g. due to abandoned screen) 5. Poor diagnostic quality imaging (which would be repeated) 6. Non-standard acquisitions beyond the routine 4 screening views 7. For negative or benign cases, women without a negative follow up screen approximately 3 years later (at least 31 months after initial screen), as this would preclude determination of a robust ground truth Part B: Same dataset as defined in Part A, with the same inclusion and exclusion criteria |
| Recruitment start date | 21/03/2022 |
| Recruitment end date | 21/03/2024 |
Locations
Countries of recruitment
- England
- United Kingdom
Study participating centres
Teddington
TW11 0JL
United Kingdom
Surbiton
KT6 6EZ
United Kingdom
2 Edridge Road
Croydon
CR9 1PJ
United Kingdom
Sutton
SM1 2RJ
United Kingdom
London
SW15 5PN
United Kingdom
Purley
CR8 2YL
United Kingdom
London
W6 8RF
United Kingdom
London
W2 1NY
United Kingdom
Southall
UB1 3HW
United Kingdom
Hounslow
TW3 3LH
United Kingdom
Uxbridge
UB8 1UB
United Kingdom
Sponsor information
University/education
Exhibition Road
South Kensington
London
SW7 2BX
England
United Kingdom
| Phone | +44 (0)20 7594 9480 |
|---|---|
| rgit.ctimp.team@imperial.ac.uk | |
| Website | http://www.imperial.ac.uk/ |
"ROR" |
https://ror.org/041kmwe10 |
Funders
Funder type
Government
Government organisation / National government
- Alternative name(s)
- National Institute for Health Research, NIHR Research, NIHRresearch, NIHR - National Institute for Health Research, NIHR (The National Institute for Health and Care Research), NIHR
- Location
- United Kingdom
Results and Publications
| Intention to publish date | 21/03/2025 |
|---|---|
| Individual participant data (IPD) Intention to share | No |
| IPD sharing plan summary | Not expected to be made available |
| Publication and dissemination plan | Planned publication in a high-impact, peer-reviewed journal |
| IPD sharing plan | The data generated or analysed during the study cannot be shared at this time due to contractual agreements with study sites |
Study outputs
| Output type | Details | Date created | Date added | Peer reviewed? | Patient-facing? |
|---|---|---|---|---|---|
| HRA research summary | 28/06/2023 | No | No |
Editorial Notes
06/06/2022: Trial's existence confirmed by East Midlands - Nottingham 1 Research Ethics Committee.
