| EPSRC Reference: |
GR/L49581/01 |
| Title: |
NEURAL NETWORK ANALYSIS OF BREAST CANCER PROGNOSIS |
| Principal Investigator: |
Tarassenko, Professor L |
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| Department: |
Engineering Science |
| Organisation: |
University of Oxford |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 June 1997 |
Ends: |
31 May 1999 |
Value (£): |
109,952
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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The choice of treatment for primary breast cancer depends partly on the risk of recurrence: once the tumour has been removed, the patient may be given further treatment such as chemotherapy if the risk of recurrence is thought to justify it. The risk of recurrence must be estimated using information available at the time of initial treatment. Our research is designed to improve this estimation as an aid to decision making. We will apply neural network techniques to this problem , with the aim of improving standard statistical methods, such as Cox regression, to model more flexible non-linear relationships between the prognostic factors and the risk of recurrence. Alongside the development of flexible models, we will investigate methods of including the partial data from patients for whom some of the information is not available, both in training models and in suing them for prediction. W will also assess the importance of the different prognostic factors in order too base predictions on an effective subset of the factors considered.
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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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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ox.ac.uk |