Research Fellow in Improving the diagnosis of children's brain tumours by Functional Radiomics - Ch
- Employer
- Global Academy Jobs
- Location
- United Kingdom
- Closing date
- Feb 26, 2018
View more
- Sector
- Business and Finance, Science, Computer Science and IT, Computer Science, General Computing
- Hours
- Full Time
- Organization Type
- University and College
- Jobseeker Type
- Academic (e.g. 'Lecturer')
Job Details
Improving the diagnosis of children’s brain tumours by Functional Radiomics – Children’s Cancer and Leukaemia Group / Little Princess Trust
To create and contribute to the creation of knowledge by undertaking a specified range ofactivities within an established research programme and/or specific research project.
Background: Brain Tumours are the most common cause of death from cancer in children. Initial diagnosis is from a combination of clinical and imaging findings prior to tissue being available in about three quarters for histology and molecular analysis. Magnetic Resonance Imaging (MRI) is the standard imaging investigation but is increasingly supplemented by Functional Imaging (FI) which probes properties of the tumour and its microenvironment such as blood flow, tissue structure and chemical composition. FI improves non-invasive diagnostic accuracy and provides novel prognostic biomarkers. Emerging data shows that FI can identify new diagnostic classes.
Aim: To improve the diagnosis of children with brain tumours through the use of multi-modal FI and its analysis by computerised machine learning.
Methods: Data will be analysed from the national Children’s Cancer and Leukaemia Group’s (CCLG) Functional Imaging database which has more than 1000 brain tumour cases registered over the past 12 years. FI data is based on advanced MRI and includes perfusion, diffusion and spectroscopy acquired with a common protocol. Initial processing will be undertaken to extract key parameters which will then be used along with textural features of the conventional MRI as the input to machine learning techniques to build diagnostic tools. Diagnostic classes from the imaging will be compared with conventional diagnosis and their survival characteristics determined. Analyses and results will be made available through an updated CCLG database.
Outcomes: This will improve diagnosis from imaging and determine whether new diagnostic classes can be defined by their FI characteristics.
Person Specification
- First degree in area of specialism and normally, a higher degree relevant to research area or equivalent qualifications
- High level analytical capability
- Ability to communicate complex information clearly
- Fluency in relevant models, techniques or methods and ability to contribute to developing new ones
- Ability to assess resource requirements and use resources effectively
- Understanding of and ability to contribute to broader management/administration processes
Application Deadline
26 Feb 2018
Company
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