Plain English Summary
Background and study aims
Breast cancer is one of the most frequently occurring cancer types in women worldwide. Breast screening is considered to be the world-wide gold standard for early detection and control of breast cancer. Mammography reading for breast screening is known to be a laboriously repetitive and meticulous task, and many sites struggle to meet required performance targets (NHS, EU), Radiologists currently have no effective practical support tools for reading mammography images; however, with the application of leading-edge deep learning techniques, the sponsor has developed software for the accurate analysis of mammograms to support the diagnosis of breast cancer.
This study aims to calibrate and validate the software's performance on retrospective (historic) data from NHS trusts and mammography units. By doing this study, we will learn how effective and generalisable the software can be in supporting radiologists in breast screening in the UK.
Who can participate?
Being a retrospective study, no patients will be directly involved in the study, and there will be no effect or change to any patient's care. The study will evaluate the software based on its analysis of validated de-identified historical cases from investigational sites where the outcomes (i.e. biopsy results, normal follow-up) are already known.
What does the study involve?
Historic data from NHS trusts and mammography units will be fed into the software to test functionality and performance.
What are the possible benefits and risks of participating?
Not applicable
Where is the study run from?
Kheiron Medical Technologies, London
When is the study starting and how long is it expected to run for?
September 2018 to September 2019
Who is funding the study?
1. Innovate UK
2. Kheiron Medical Technologies
Who is the main contact?
Dr Hugh Harvey
Trial website
Contact information
Type
Scientific
Primary contact
Dr Hugh Harvey
ORCID ID
http://orcid.org/0000-0003-4528-1312
Contact details
Rocketspace
40 Islington High St
London
N1 8EQ
United Kingdom
Type
Scientific
Additional contact
Dr Nisha Sharma
ORCID ID
Contact details
Leeds Teaching Hospital NHS Trust
Leeds
LS9 7TF
United Kingdom
0113 243 3144
nisha.sharma2@nhs.net
Additional identifiers
EudraCT number
Nil known
ClinicalTrials.gov number
Nil known
Protocol/serial number
AUX-07-2018-KMT
Study information
Scientific title
A retrospective multi-centre clinical investigation of a novel medical technology solution in the assessment of mammography images
Acronym
Study hypothesis
The purpose of the study is to calibrate and validate a deep learning software system that provides breast radiologists with recall decision support in a breast screening setting. The performance of this software in supporting recall decisions in breast screening is validated on retrospective data from multiple sites from a cohort of women who have undergone routine mammographic screening for breast cancer and have sufficient follow-up imaging or biopsy data.
Ethics approval
Approved 03/10/2018, HRA and Health and Care Research Wales (HCRW) (Castle Bridge 4, 15-19 Cowbridge Rd E, Cardiff CF11 9AB; 029 2023 0457; hra.approval@nhs.net), Ref: 19/HRA/0376
Study design
Multi-site retrospective cohort study
Primary study design
Observational
Secondary study design
Case-control study
Trial setting
Hospitals
Trial type
Screening
Patient information sheet
None available
Condition
Breast cancer
Intervention
The intervention is the sponsor's deep learning software, assessed on de-identified randomised retrospective breast screening cases and outcomes. Comparison is made against the control arm of existing reference outcomes within the retrospective dataset where the deep learning software was not in use.
Intervention type
Device
Phase
Not Applicable
Drug names
Primary outcome measure
Rate of detection of malignancy of the Sponsor’s deep learning software measured using patient notes.
Secondary outcome measures
Secondary aims will assess the software's performance (sensitivity, specificity, percent indeterminate) at varying settings as well as measure its recall rate (percentage of screening mammograms recalled for further assessment). Accuracy is measured in terms of sensitivity (true positive rate), specificity (true negative rate) as compared to a defined Reference Standard, plotted onto Receiver Operator Curves, and an Area Under the Curve (AUC) calculated. Recall rate is the rate of positive reported findings in a given sample.
Overall trial start date
01/06/2018
Overall trial end date
01/09/2019
Reason abandoned (if study stopped)
Eligibility
Participant inclusion criteria
1. Female patients
2. Mammography cases for screening purposes, i.e. cases from:
a) patients involved in the national breast screening program (depending on the jurisdiction includes women of age
45-73 who are called for examination via a letter by the national health authorities based on the population database),
and
b) women outside the national breast screening program who decided on their own to participate as per standard of
care
3. Cases with images in DICOM format
4. Cases with images produced by certified digital mammography hardware
5. Cases with one set of all of the 4 standard mammography images (i.e. exactly one of each: MLO-R, MLO-L, CC-R,
CC-L) present (no images missing and no extra images)
6. Cases with available historical outcome information as specified below*:
(Outcome information:
Confirmed positive case: malignancy is confirmed by a decisive biopsy, cytology or histology of the surgical specimen
within 250 days after the time of the image acquisition date.
Confirmed negative case: a negative follow-up result is available at least 34 months after the image acquisition date
(with no malignant operation and no malignancy indication in that period.)
*This inclusion criteria only applies to sensitivity/specificity analysis (not recall rate analysis)
Participant type
Healthy volunteer
Age group
Adult
Gender
Female
Target number of participants
up to 1,000,000
Participant exclusion criteria
1. Male patients
2. Images that are non-original images (e.g. post-processed images)
3. Magnified images (in the DICOM file the View Modifier Code Sequence (0054, 0222) has either of the values: R-
102D6, “Magnification” or R-102D7, “Spot compression”)
4. Cases with indication of a breast operation due to malignancy in the past medical history
5. Cases dated after a breast cancer confirmed by biopsy, cytology or histology
6. All patients of whom any image data was used during training, calibration or testing during the technology
development of the deep learning model.
Note: hormone replacement therapy in the past medical history is not an exclusion criterion.
Recruitment start date
01/09/2018
Recruitment end date
01/09/2019
Locations
Countries of recruitment
United Kingdom
Trial participating centre
Leeds Teaching Hospitals NHS Trust
St. James's University Hospital
Beckett Street
Leeds
LS9 7TF
United Kingdom
Trial participating centre
Nottingham University Hospitals NHS Trust Headquarters
Queens Medical Centre
Derby Road
Nottingham
NG7 2UH
United Kingdom
Trial participating centre
United Lincolnshire Hospitals NHS Trust
Lincoln County Hospital
Greetwell Road
Lincoln
LN2 4AX
United Kingdom
Trial participating centre
Manchester University NHS Foundation Trust
Cobbett House
Oxford Road
Manchester
M13 9WL
United Kingdom
Sponsor information
Organisation
Kheiron Medical Technologies
Sponsor details
Rocketspace
40 Islington High St
London
N1 8EQ
United Kingdom
+447713256495
hugh@kheironmed.com
Sponsor type
Industry
Website
Funders
Funder type
Government
Funder name
Innovate UK
Alternative name(s)
Funding Body Type
government organisation
Funding Body Subtype
Federal/National Government
Location
United Kingdom
Funder name
Kheiron Medical Technologies
Alternative name(s)
Funding Body Type
Funding Body Subtype
Location
Results and Publications
Publication and dissemination plan
Peer reviewed publication is anticipated, alongside academic conference scientific presentations. Results will be submitted to regulatory authorities for the purposes of medical device certification.
IPD sharing statement:
The current data sharing plans for this study are unknown and will be available at a later date
Intention to publish date
01/12/2019
Participant level data
To be made available at a later date
Basic results (scientific)
Publication list