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Details of Grant 

EPSRC Reference: EP/P023509/1
Title: Intelligent and Personalised Risk Stratification and Early Diagnosis of Lung Cancer
Principal Investigator: Schnabel, Professor JA
Other Investigators:
Glocker, Professor B Desai, Dr S
Researcher Co-Investigators:
Project Partners:
Guy's & St Thomas' NHS Foundation Trust Kings Healthcare Siemens
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 01 October 2017 Ends: 30 September 2021 Value (£): 947,232
EPSRC Research Topic Classifications:
Medical Imaging
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Feb 2017 HIPs 2017 Panel Meeting Announced
Summary on Grant Application Form
Lung cancer is the second most common cancer in both males and females, and has a very poor prognosis, causing >35,000 of cancer-related deaths each year (nearly 100 every day). This is due to the mostly very late-stage diagnosis of cancer: nearly 50% of all lung cancer cases are only diagnosed at very late Stage IV where no curative treatment exists. The annual cost of lung cancer to the UK economy is estimated to be around £2.4 billion, taking into account the cost of treatment and premature death, the cost to business of sick leave and of unpaid care by friends and family. It eclipses the cost of any other cancer, and continues to present a significant economic and healthcare burden. There is currently no national lung cancer screening programme in the UK, as current tests are deemed to be inadequate, and not outweighing risks associated with screening that involves radiation exposure.

The vision for our Healthcare Impact Partnership is to pave the way for a fundamentally different approach in which lung cancer can be detected at an early stage in patients identified as being at high risk of developing lung cancer: In a close three-way partnership between clinicians specialising in lung disease, healthcare industry, and computational imaging/machine learning researchers, we will jointly develop, test and clinically evaluate new computational methods for detecting, classifying and monitoring lung nodules in low-dose longitudinal CT of patients identified of being at risk of developing lung cancer. Specifically, we will embed powerful machine learning methods within a lung registration framework that has been previous developed using EPSRC funding. Through the use of deep feature learning techniques as well as through learning complex respiratory motion patterns, we will explore transfer learning from large, annotated lung CT image databases to a high-risk patient cohort receiving low-dose CT imaging.

Through this partnership, which brings together expertise in computational imaging and deep learning, expert clinicians and a leading CT manufacturer, we aim to achieve both computational innovations in personalised diagnostics, as well as a strong translational focus for improved patient management, effectively leading the way in providing advanced healthcare technologies for a future UK lung cancer screening programme.

Key Findings
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