| EPSRC Reference: |
EP/P023509/1 |
| Title: |
Intelligent and Personalised Risk Stratification and Early Diagnosis of Lung Cancer |
| Principal Investigator: |
Schnabel, Professor JA |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Imaging & Biomedical Engineering |
| Organisation: |
Kings College London |
| Scheme: |
Standard Research |
| Starts: |
01 October 2017 |
Ends: |
30 September 2021 |
Value (£): |
947,232
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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06 Feb 2017
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HIPs 2017 Panel Meeting
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Announced
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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