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Use of highly multi-modal PET and MRI data as a predictive biomarker of the site of recurrence in neuro-oncology


Project Description

Primary brain tumour including glioblastoma (GBM), the most aggressive and most common form in adults, continue to have a socioeconomic impact that is disproportionate to the tumour frequency due to our inability to obtain a cure in the vast majority of adult sufferers. Even after radiologically complete resection of the enhancing component, recurrence typically occurs within 2cm of the original tumour cavity despite adjuvant radiotherapy planned to include this zone.

Current clinical diagnostic neuroimaging techniques typically rely upon magnetic resonance imaging (MRI) with anatomical sequences commonly augmented by diffusion-weighted acquisitions and Gadolinium contrast enhancement. Advanced MRI and Positron Emission Tomography (PET) techniques have been used to acquire quantitative imaging data that may improve accuracy in tumour delineation and characterisation. These techniques include tissue relaxivity and diffusivity characteristics using MRI, and amino acid transport and translocator protein (TSPO) expression using PET for which the research team has extensive experience. Preliminary studies suggest that these advanced imaging techniques may be capable of identifying tumour involved regions at presentation that will later become the site of recurrence either because they are inadequately treated or are inherently more treatment resistant. This may provide valuable information to improve treatment planning for these patients.

This PhD project will build upon an existing dataset of baseline scans in patients with glioblastoma that includes PET with amino acid and TSPO tracers as well as MRI including diffusion tensor, dynamic susceptibility and dynamic contrast enhanced sequences. Follow-up data will be acquired during the project to review the response to treatment and ultimately the site of recurrence. The highly multi-modal data will be utilised to evaluate tumour delineation with the differing techniques individually and in combination. Potential biomarkers of tumour infiltration or treatment resistant cell populations will be identified on pre-treatment imaging by comparison to the ultimate site of recurrence.

Training/techniques to be provided:
This PhD studentship is based in biomedical imaging science with an emphasis on data analysis methodology for tissue classification, tumour edge detection and multi-parametric mapping. It provides training opportunities in image processing and segmentation, tracer kinetic analysis and statistical imaging methods. It is embedded in an interdisciplinary research environment of clinical scientists and imaging experts based at the Imaging Facilities at the University of Manchester with close links to the Salford Royal NHS Foundation Trust and other hospitals within the Manchester Academic Health Sciences Centre. It is anticipated that this project would lead to a number of methodological and clinical peer-reviewed publications. As part of the training, the student will be encouraged to attend relevant course units from the MSc in Medical Imaging Science.

Entry Requirements:
Candidates are expected to hold, or be about to obtain, an Upper Second-class Honours degree (or overseas equivalent) in a related subject area for entry to a PhD programme. A Lower Second-class Honours degree may be considered if applicants also hold a Master’s degree with a Merit classification. Candidates with experience in neuroimaging or with an interest in neuro-oncology are encouraged to apply. Experience of medical image analysis and/or relevant programming skills would be desirable.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Rapp M et al. Recurrence pattern analysis of primary glioblastoma. World Neurosurgery 2017;130:733-740. DOI: 10.1016/j.wneu.2017.04.053

Jensen P et al. TSPO Imaging in Glioblastoma Multiforme: A Direct Comparison Between 123I-CLINDE SPECT, 18F-FET PET, and Gadolinium-Enhanced MR Imaging. Journal of Nuclear Medicine 2015;56:1386–1390. DOI: 10.2967/jnumed.115.158998

Bosnyák E et al. Tryptophan PET predicts spatial and temporal patterns of post-treatment glioblastoma progression detected by contrast-enhanced MRI. Journal of Neuro-Oncology 2016;126(2):317-325. DOI: 10.1007/s11060-015-1970-3

Price S et al. Predicting patterns of glioma recurrence using diffusion tensor imaging. European Radiology 2007;17:1675–1684. DOI: 10.1007/s00330-006-0561-2

Lohmann P et al. Combined Amino Acid Positron Emission Tomography and Advanced Magnetic Resonance Imaging in Glioma Patients (review). Cancers (Basel) 2019;11(2):153. DOI: 10.3390/cancers11020153

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