On World Cancer Day 2022, read how Optellum is working to change the survival rates for lung cancer with an innovative AI platform.
DART is pleased to be partnering with Optellum and others to build and strengthen Artificial Intelligence algorithms for the early diagnosis of lung cancer and other lung conditions. Here Optellum describe their efforts to redefine early intervention of diseases like lung cancer, by enabling every clinician, in every hospital, to make the right decisions and give their patients the best chance to fight back.
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:
Enhanced information about patient outcomes
New digital pathology AI and AI derived radiomics for diagnosis and stratification of patients
Algorithms to better evaluate risks from comorbidities such as chronic obstructive pulmonary disease (COPD)
New insights about the right at-risk individuals to be selected for screening, using linked data from primary care
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:
Clinical data
CT scans (LDCT and PET-CT) and
Digitised images of stained tissue sections (digital pathology)
Portsmouth encourages participation in Lung Health Checks
People over 55 but younger than 75 who have ever smoked are being offered a free lung health check. See a video here or read NHS news items here and here.
This is part of an expanding lung cancer screening programme.
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
Registration with ISRCTN
DART has been registered with ISRCTN, using ID 13720905
This includes a Plain English summary under the title “Providing data so computer systems can help with the early identification of lung diseases, leading to more rapid treatment and better survival rates”.
About ISRCTN
Registration with ISRCTN is the first step towards trial transparency and the future dissemination of health research outcomes. Its key aim is to ensure that all healthcare decisions are informed by all of the available evidence, thus, overcoming publication bias and selective reporting. Registration provides opportunities for collaboration and reduces duplication of research efforts; it also improves awareness of trials for clinicians, researchers, patients and the public.
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
NIHR
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.
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.
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.”
