Providing data so computer systems can help with the early identification of lung diseases, leading to more rapid treatment and better survival rates

Plain English Summary

https://www.cancerresearchuk.org/about-cancer/find-a-clinical-trial/a-study-to-develop-and-test-a-computer-programme-to-help-to-improve-the-diagnosis-of-lung-cancer#undefined (added 05/05/2022)

Background and study aims
In the UK, lung cancer is common with a very low 5-year survival rate as most patients are diagnosed at a late stage. Early detection on a CT scan when the cancers are small and seen as a nodule has been shown to improve survival.

DART will work with NHS England’s ambitious Lung Cancer Screening programme using CT to collect clinical, CT and histology data for research aimed at improving lung cancer diagnosis and screening using artificial intelligence, AI.

If DART is successful, using artificial intelligence we will speed up the time to diagnose lung cancer whilst also identifying incidental harmless nodules on CT. DART aims to: remove the need for other investigations such as lung biopsies, making investigations safer and quicker; help pathologists diagnose lung cancer using; help patients by providing their doctors with more information on lung and heart function; improve patient selection for lung cancer screening.

DART aims to improve screening using AI, resulting in the avoidance of additional tests and biopsies which cause great patient anxiety, take time and are expensive.

DART will develop an AI algorithm for histology so that specimens from lung biopsies and resections can also be analysed in a similar fashion to CT scans.

Patients with lung cancer often have damaged lungs from smoking making surgery or radiation treatment unsafe. DART plans to develop an AI technique that can be used on all lung CT scans performed. As smoking can cause heart disease, patients screened for lung cancer often have heart disease. DART aims to use AI to see if we can identify this from their CT scans.

We will develop a specific risk model for Lung Cancer Screening selection, that outperforms published risk models that have been developed in academic institutions but are not used in clinical practice.

Who can participate?
To aid our research, it is important to gather data from as many people attending Lung Health Checks as possible. However, if you do not want your data included, now or at any time, please tell us using the contact details below

What does the study involve?
Computers will be to conduct additional analysis of scans and data from those attending lung health checks. It will not require any extra time or visits and will not interfere in any way with the standard health care.

Personal information will be kept private, but an NHS research laboratory will be able to link a patient’s your data (health records, scans, biopsies and resections) accurately.

What are the possible benefits and risks of participating?
There are no health risks to participating. We will anonymise data by removing the code before it is used by researchers so there is no link back to patients, who will never be identified in research or publications.

There are no immediate benefits to participants, but participation will contribute towards:
• If found at an early stage, lung cancer is curable
• DART will develop an Artificial Intelligence software programme that is faster and accurate to assist doctors to interpret CT scans and detect cancer
• This will speed up the time to diagnosis and reduce the numbers of additional scans and biopsies that might be needed in future.
• As smoking can cause heart disease, patients screened for lung cancer often have heart disease, and we aim to use AI to see if we can identify this from their CT scans as well.

Where is the study run from?
The study is run from the University of Oxford (UK)

When is the study starting and how long is it expected to run for?
Data will be collected from lung health checks between 1st October 2020 and 31st July 2023

Who is funding the study?
The study is funded by UK Research and Innovation

Who is the main contact?
Prof Fergus Gleeson, Professor of Radiology, University of Oxford, fergus.gleeson@oncology.ox.ac.uk

Study website

Contact information

Prof Fergus Gleeson
Principal Investigator

Department of Oncology
University of Oxford
Old Road Campus
Research Building
Oxford
OX3 7DQ
United Kingdom

ORCID ID	0000-0002-5121-3917
Email	fergus.gleeson@oncology.ox.ac.uk

Study information

Eligibility

Locations

Countries of recruitment

• England
• United Kingdom

Study participating centres

Sponsor information

Research Governance, Ethics & Assurance Team (RGEA), University of Oxford
University/education

Joint Research Office
1st floor, Boundary Brook House
Churchill Drive
Headington
Oxford
OX3 7GB
England
United Kingdom

Email	ctrg@admin.ox.ac.uk
Website	https://www.ukri.org/
"ROR"	https://ror.org/001aqnf71

Funders

Funder type

Government

Results and Publications

Study outputs

Editorial Notes

05/05/2022: Internal review.
05/05/2022: The Cancer Research UK plain English summary has been added.
20/04/2022: The ethics approval has been added.
04/03/2022: Trial's existence confirmed by the NHS HRA