Condition category
Cancer
Date applied
06/03/2019
Date assigned
20/03/2019
Last edited
21/06/2019
Prospective/Retrospective
Retrospectively registered
Overall trial status
Ongoing
Recruitment status
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 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

http://www.kheironmed.com

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

Publication citations

Additional files

Editorial Notes

21/06/2019: The plain English summary was added. 05/04/2019: Internal review. 07/03/2019: Trial’s existence confirmed by IRB