Condition category
Date applied
Date assigned
Last edited
Retrospectively registered
Overall trial status
Recruitment status
No longer recruiting

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 July 2020

Who is funding the study?
1. Innovate UK
2. Kheiron Medical Technologies

Who is the main contact?
Ms Bojana Selinsek

Trial website

Contact information



Primary contact

Ms Bojana Selinsek


Contact details

Stylus Building
112 Old Street London
United Kingdom



Additional contact

Dr Nisha Sharma


Contact details

Leeds Teaching Hospital NHS Trust
United Kingdom
0113 243 3144

Additional identifiers

EudraCT number

Nil known number

Nil known

Protocol/serial number


Study information

Scientific title

A retrospective multi-centre clinical investigation of a novel medical technology solution in the assessment of mammography images


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;, Ref: 19/HRA/0376

Study design

Multi-site retrospective cohort study

Primary study design


Secondary study design

Case-control study

Trial setting


Trial type


Patient information sheet

None available


Breast cancer


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



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


Overall trial end date


Reason abandoned (if study stopped)


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),
b) women outside the national breast screening program who decided on their own to participate as per standard of
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




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


Recruitment end date



Countries of recruitment

United Kingdom

Trial participating centre

Leeds Teaching Hospitals NHS Trust
St. James's University Hospital Beckett Street
United Kingdom

Trial participating centre

Nottingham University Hospitals NHS Trust Headquarters
Queens Medical Centre Derby Road
United Kingdom

Trial participating centre

United Lincolnshire Hospitals NHS Trust
Lincoln County Hospital Greetwell Road
United Kingdom

Sponsor information


Kheiron Medical Technologies

Sponsor details

Stylus Building
112-116 Old Street
United Kingdom

Sponsor type




Funder type


Funder name

Innovate UK

Alternative name(s)

Funding Body Type

government organisation

Funding Body Subtype

National government


United Kingdom

Funder name

Kheiron Medical Technologies

Alternative name(s)

Funding Body Type

Funding Body Subtype


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


Participant level data

To be made available at a later date

Basic results (scientific)

Publication list

Publication citations

Additional files

Editorial Notes

05/08/2020: The following changes were made to the trial record: 1. The overall end date was changed from 31/07/2020 to 30/09/2020. 2. The intention to publish date was changed from 01/09/2020 to 30/12/2020. 08/07/2020: The following changes were made to the trial record: 1. The overall trial end date was changed from 30/06/2020 to 31/07/2020. 2. The intention to publish date was changed from 01/06/2020 to 01/09/2020. 07/05/2020: The following changes have been made: 1. The overall trial end date has been changed from 31/03/2021 to 30/06/2020. 2. A trial contact has been updated. 3. The plain English summary has been updated accordingly. 13/03/2020: The following changes have been made: 1. The sponsor address has been updated. 2. The overall trial end date has been changed from 01/09/2019 to 31/03/2021. 3. The intention to publish date has been changed from 01/12/2019 to 01/06/2020. 4. The trial participating centre "Manchester University NHS Foundation Trust" has been removed. 5. The plain English summary has been updated to reflect the changes above. 21/06/2019: The plain English summary was added. 05/04/2019: Internal review. 07/03/2019: Trial’s existence confirmed by IRB