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Comparing imaging techniques to diagnose lung cancer – looking for advisors

Are you interested in helping research to improve accuracy in earlier diagnosis of lung cancer from different imaging types?

We’re looking for people who’ve had imaging for suspected lung cancer whether or not they’ve gone on to be diagnosed, as well as those who’ve been diagnosed and treated.

What is it for?

A group led by the University of Oxford is planning a research study exploring whether a CT scan can diagnose lung cancer earlier than the chest x-ray currently performed when lung cancer is suspected. As part of this research, the team is inviting people to join a patient and public involvement (PPI) advisory group.

What is the purpose of the PPI advisory group? 

The group’s purpose is to ensure the experiences and views of those referred for chest imaging for suspected lung cancer, and those diagnosed with lung cancer or their family members/carers are taken into account when planning and delivering this study.

What would PPI advisory group members be expected to do?

PPI Group members will be asked to attend up to 3 workshops, review documents being designed for study participants, join a study steering committee and help share the final study results. You can choose to take part in all or some of these activities. 


The first workshop is expected to be held in June/July 2023 during the preparation of the final funding application to the National Institute for Health and Care Research (NIHR).

If funded, the study will start early 2024 and there will be an opportunity to continue participation in the PPI advisory group at that point.


You will receive payment for any time spent in these activities and travel expenses for any face to face meetings, although most of the work will be completed remotely.

If you are interested and would like more information please contact

In-coming data

NHS Trusts which host Targeted Lung Health Checks are signing up to and contributing data to DART.

We are receiving data from:

We have received:

SCOOT, DART’s companion project, has collected 94 blood samples.

Presentation at OxCODE Symposium

Dr Weiqi Liao presented a Lightning Talk at the OxCODE (Oxford Centre for Early Cancer Detection) symposium 2022 on Tuesday 13th September 2022 at Worcester College. His talk was entitled “Predicting the future risk of lung cancer: Development and validation of QCancer2 (10-year risk) lung model and evaluating the performance of nine prediction models”

View his poster and slides.

Data collection has begun

Congratulations to those sites which have been able to begin data transfer to DART. We have received over 38,000 records already and look forward to more sites being able to contribute shortly.

DART scientists will use the data gathered by the Targeted Lung Health Checks during patient screening and care to develop:

Collectively this will define a new set of standards for lung cancer screening, to increase the number of lung cancers diagnosed earlier, and therefore treated more successfully and with fewer invasive clinical procedures.

Data collected includes:

Celebrate Doncaster LHC’s success

The Doncaster programme has been operational since mid-March 2021 and in the first year 11,857 lung health check calls and more than 5,000 initial scans have taken place; 50 lung cancers have been found, 73% of which were early-stage lung cancers.  Seven other cancers have also been confirmed and  41 patients (75%) have been given life-saving cancer treatment so far.

Read more about this pioneering lung screening trial that is saving lives in Doncaster here

Optellum attains CE marking in Europe

DART partner Optellum has attained CE marking in Europe. 

This latest certification will allow for use in the European Union (EU) and the United Kingdom (UK), and opens the door to a European expansion for the growing company. It is the latest milestone for Optellum, which received FDA 510(k) clearance in early 2021 as the first AI-assisted diagnosis application for lung cancer. You can read the full press release online here.

In addition, researchers from the University of Oxford and Oxford University Hospitals NHS Foundation Trust (OUH) have published the results of an academic study in European Radiology which takes a closer look at the Optellum Lung Cancer Prediction (LCP) model. You can read the full story on Optellum’s website here.

Approvals and adoption


DART is very pleased that the project has been adopted onto the NIHR portfolio and is therefore deemed eligible for NIHR Clinical Research Network support. Further information about CRN support can be found on the NIHR website.

The 15 Local Clinical Research Networks (LCRN) cover the length and breadth of England and are available to coordinate and support the delivery of research across the NHS in England.

Health Research Authority HRA

DART has had confirmation that HRA and Health and Care Research Wales (HCRW) Approval has been given for the study, on the basis described in the application form, protocol, supporting documentation and clarifications requested and received.

DART has also had notification of Confidentiality Advisory Group (CAG) conditional support, as per the excerpt from their letter below.

“The role of the Confidentiality Advisory Group (CAG) is to review applications submitted under these Regulations and to provide advice to the Health Research Authority on whether application activity should be supported, and if so, any relevant conditions. This application was considered at the CAG meeting held on 10 February 2022.

Health Research Authority decision

The Health Research Authority, having considered the advice from the Confidentiality Advisory Group as set out below, has determined the following:

The application, to allow the disclosure of confidential patient information from participating trusts to the Oxford University Hospitals NHS Foundation Trust, is conditionally supported, subject to compliance with the standard and specific conditions of support.

Please note that the legal basis to allow access to the specified confidential patient information without consent is now in effect.”

The conditions of support allow for the project to proceed as planned and will be addressed by mid-May 2022. DART will comply with the HRA annual review.

CAG reference: 22/CAG/0010

IRAS project ID: 301420

REC reference: 21/WM/0278

GE Healthcare and Optellum join forces to advance Lung Cancer Diagnosis with Artificial Intelligence

DART partners GE Healthcare and Optellum announced that they have signed a letter of intent to collaborate to advance precision diagnosis and treatment of lung cancer. GE Healthcare is a global leader in medical imaging solutions. Optellum is the leader in AI decision support for the early diagnosis and optimal treatment of lung cancer.

A clinician’s AI-assisted diagnosis of malignancy may enable patients whose nodules are not malignant to avoid unnecessary and aggressive procedures such as biopsy and surgical resection, while expediting the diagnostic process, and enabling the right treatment to start earlier. This has the potential to provide patients with personalized diagnosis and treatment plans, enabling lung cancer patients to be treated at the earliest possible stage when survival rates are the highest. This is also the ambition of DART.

Read the full press release here.

Reviewing a lung scan on a computer

NCIMI: Seven Strategies for Success – for adoption and deployment of Artificial Intelligence in Medical Diagnostics

NCIMI, working with The Behavioural Architects have created a guide to support their ambition of driving greater adoption of AI within the healthcare system, through the application of behavioural science. 

Read more and download the guide here.

Automated Annotator: Capturing Expert Knowledge

A paper on “Automated Annotator: Capturing Expert Knowledge for Free” by the DART digital pathology and radiomics team was presented at EMBC, the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society Intervention, November 1-5, 2021. EMBC is the flagship conference of the IEEE EMB Society and the peer-reviewed conference proceedings attract submission of innovative work through the international gathering. 

Paper Abstract: Deep learning enabled medical image analysis is heavily reliant on expert annotations which is costly. We present a simple yet effective automated annotation pipeline that uses autoencoder based heatmaps to exploit high level information that can be extracted from a histology viewer in an unobtrusive fashion.  By predicting heatmaps on unseen images the model effectively acts like a robot annotator.  The method is demonstrated in the context of coeliac disease histology images in this initial work, but the approach is task agnostic and may be used for other medical image annotation applications. The results are evaluated by a pathologist and also empirically using a deep network for coeliac disease classification. Initial results using this simple but effective approach are encouraging and merit further investigation, especially considering the possibility of scaling this up to a large number of users.

Co-author, Tapabrata (Rohan) Chakraborty said “We present a pilot demonstration to show how AI maybe be used to learn the annotations of pathologists from a relatively few examples without interrupting their workflow, and then use that knowledge to annotate new images in an automated fashion, thus reducing time and cost. This paper is based on an earlier work on coeliac disease histology data by current DART members and past students, and it is being exploited and extended as a generalised approach in the current DART project on lung histology data.”