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Details of Grant 

EPSRC Reference: EP/N021967/1
Title: Non-invasive MRI Biomarkers for Oncology
Principal Investigator: Panagiotaki, Dr E
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: UCL
Scheme: EPSRC Fellowship
Starts: 01 July 2016 Ends: 31 March 2023 Value (£): 992,539
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
25 Feb 2016 Eng Fellowship Interview Feb 2016 (B) Announced
09 Feb 2016 Engineering Prioritisation Panel Meeting 9 and 10 February 2016 Announced
Summary on Grant Application Form
This project develops innovative Magnetic Resonance Imaging (MRI) methods to reveal new non-invasive markers of cancer pathology. In particular the research programme develops biophysical models of tumour tissue that support non-invasive estimates of key characteristics of tissue cellular architecture including those that currently guide diagnosis, grading, and treatment assignment through classical histology. These cellular characteristics include cell size, cell shape, cell density, similar features of subcellular structures, and potentially new features that histology cannot reveal such as cell membrane permeability and cell cytoplasm viscosity. In contrast to classical histology, such an approach avoids unnecessary interventions like biopsies. Moreover, the models combine with macroscopic imaging to provide maps of histological features over the whole organ, thus evading false-positives from poor biopsy targeting. In contrast to current imaging techniques, the new model-based imaging techniques provide much greater specificity associating contrast with particular tissue changes thus providing much better information on which to assign treatment. The ultimate goal is to enable non-invasive cancer detection, staging and grading entirely through harmless imaging.

The project will leverage the advances of a method I recently devised, called VERDICT, which uses diffusion MRI to provide an early demonstration of the power of such a model-based imaging technique in estimating cancer-tissue microstructure non-invasively. This project builds on these ideas to achieve the performance levels required to allow robust detection, grading and staging for establishing non-invasive imaging as the primary cancer diagnostic method. The project exploits advances in computational methods and in MRI technology to improve sensitivity and specificity dramatically. Specifically: 1) sophisticated optimisation techniques will enhance performance and practical utility of non-invasive model-based imaging; 2) advanced MRI sequences will allow access to new and important cancer microstructural features, such as the morphology of cell nuclei, and 3) alternative contrast methods will extend the methods to other MRI modalities beyond diffusion for complementary and additional cancer malignancy information. Although the project focuses on prostate cancer, it also aims to initiate follow-on projects to explore applications to other cancers such as brain, breast and bone metastasis.

This fellowship is a significant stepping-stone for my career in fighting cancer. My aspiration is for patients to have a quick, safe and comfortable clinical cancer examination with determinate diagnosis. Towards this vision the fellowship will allow me to capitalise on my recent progress on this idea, exploiting my current expertise in diffusion MRI, and its application in cancer. It provides me the opportunity to gain experience of leading my own multidisciplinary project and research team thus establishing myself in the UK and internationally as a leader on that topic. Moreover, the fellowship will allow me to broader my skills and collaborations to the wider medical imaging arena and variety of clinical applications in cancer. This underpins my development towards an international academic leader in the fields of computational imaging and cancer research and management.

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