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”
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:
CT scans (LDCT and PET-CT) and
Digitised images of stained tissue sections (digital pathology)
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.”
UKRI announces £11 million funding for DART lung health project
DART (the Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases) is the latest project to join the NCIMI programme.
UK Research and Innovation, Cancer Research UK and industry are investing more than £11 million in an Oxford-led artificial intelligence (AI) research programme to improve the diagnosis of lung cancer and other thoracic diseases
Professor Fergus Gleeson at the University of Oxford will lead on a programme of research focusing on accelerating pathways for the earlier diagnosis of lung cancer. Lung cancer is the biggest cause of cancer death in the UK and worldwide, with £307 million/year cost to the NHS in England.
The earlier that lung cancer is diagnosed, the more likely that treatment will be successful but currently only 16% patients are diagnosed with the earliest stage of the disease. To address this clinical problem, NHS England is launching a £70 million lung cancer screening pilot programme at 10 sites.
To improve patient care beyond the current screening guidelines, a team of academics from Oxford University, Nottingham University, and Imperial College London; NHS clinicians from Oxford University Hospitals NHS Trust, Nottingham University Hospitals NHS Trust, the Royal Marsden Hospital, the Royal Brompton Hospital, and University College London Hospitals NHS Foundation Trust; and the Roy Castle Lung Cancer Foundation will join forces with three leading industrial partners (Roche Diagnostics, GE Healthcare, Optellum).
Working with the NHS England Lung Health Check programme, clinical, imaging and molecular data will be combined for the first time using AI algorithms with the aim of more accurately and quickly diagnosing and characterising lung cancer with fewer invasive clinical procedures. Algorithms will also be developed to better evaluate risks from comorbidities such as chronic obstructive pulmonary disease (COPD).
In addition, this programme will link to data from primary care to better assess risk in the general population to refine the right at-risk individuals to be selected for screening. It is hoped that this research will define a new set of standards for lung health and cancer screening to increase the number of lung cancers diagnosed at an earlier stage, when treatment is more likely to be successful.
DART and NCIMI
This programme builds on the National Consortium of Intelligent Medical Imaging (NCIMI) at the Big Data Institute in Oxford, one of five UK AI Centres of Excellence.
The funding, delivered through UK Research and Innovation’s (UKRI’s) Industrial Strategy Challenge Fund, is part of over £13m government investment in ‘data to early diagnosis and precision medicine’ for the research, development and evaluation of integrated diagnostic solutions. UKRI is also partnering with Cancer Research UK, which is making up to a £3m contribution to the cancer-focused projects. The Oxford-led project is one of six awarded from this competition.