Fetit, Ahmed E. (2015) Radiomics in paediatric neuro-oncology : MRI textural features as diagnostic and prognostic biomarkers. PhD thesis, University of Warwick.

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Abstract

Motivation:
Brain and central nervous system tumours form the second most common group of cancers in children in the UK, accounting for 27% of all childhood cancers. Despite current advances in magnetic resonance imaging (MRI), non-invasive characterisation of paediatric brain tumours remains challenging. Radiomics, the high-throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterisation and decision support.

Aim and Methods:
In search for diagnostic and prognostic oncological markers, the aim of this thesis was to study the application of MRI texture analysis (TA) for the characterisation of paediatric brain tumours. To this end, single and multi-centre experiments were carried out, within a supervised classification framework, on clinical MR imaging datasets of common brain tumour types.

Results:
TA of conventional MRI was successfully used for diagnostic classification of common paediatric brain tumours. A key contribution of this thesis was to provide evidence that diagnostic classification could be optimised by extending the analysis to include three-dimensional features obtained from multiple MR imaging slices. In addition to this, TA was shown to have a good cross-centre transferability, which is essential for long-term clinical adoption of the technique. Finally, fifteen textural features extracted from T2-weighted MRI were identified to be of significant prognostic value for paediatric medulloblastoma.

Conclusion:
It was shown that MRI TA provides valuable quantifiable information that can supplement qualitative assessments conducted by radiologists, for the characterisation of paediatric brain tumours. TA can potentially have a large clinical impact, since MR imaging is routinely used in the brain cancer clinical work-flow worldwide, providing an opportunity to improve personalised healthcare and decision-support at low cost.

Item Type:	Thesis (PhD)
Subjects:	Q Science > Q Science (General)
Library of Congress Subject Headings (LCSH):	Brain -- Tumors -- Diagnosis, Biochemical markers, Machine learning, Magnetic resonance imaging
Official Date:	July 2015
Dates:	Date Event July 2015 Submitted
Institution:	University of Warwick
Theses Department:	Warwick Manufacturing Group
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Arvanitis, Theodoros N.
Sponsors:	Warwick Manufacturing Group
Extent:	xxii, 216 leaves : illustrations (colour)
Language:	eng

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