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Dr Philip Whybra

Dr Philip Whybra

Research Associate

School of Engineering

Email
whybrap@cardiff.ac.uk
Campuses
N/1.51, Queen's Buildings - North Building, 5 The Parade, Newport Road, Cardiff, CF24 3AA

Overview

I have strong links to Cardiff University, having first graduated here with a Masters in Physics (MPhys) in 2015. After time in industry, I re-joined the School of Engineering at Cardiff as a PhD candidate in October 2016. I now continue to work within the Cancer Imaging and Data Analytics (CIDA) team and the Medical Engineering Research Group as a Research Associate.

My research interests are broadly in the topics of Medical Image Analysis, Imaging Biomarkers, and Radiomics. I am curious about all aspects of image analysis that could be used to further personalise cancer treatment.

A key aspect of my work concerns the standardisation of radiomic techniques. This is a necessity to enable transition of any promising research models to use within a clinical setting. Notably, I am a core member of an international effort known as the Image Biomarker Standardisation Initiative (IBSI) (https://theibsi.github.io).

Biography

Education and Work

  • Research Associate, Cardiff University, UK (2021-current)
  • Research Assistant, Cardiff University, UK (2021)
  • PhD  - School of Engineering, Cardiff University, UK (2016-2021)
  • Lab Demonstrator (Computational Physics, PX1224 and PX2134), Cardiff University, UK (2017-2018)
  • Intern Research Scientist, Mirada Medical, Oxford (2016)
  • MPhys - School of Physics and Astronomy, Cardiff University, UK (2011-2015)

Committees and reviewing

  • Journal Reviewer, Medical Physics

Publications

2021

2020

2019

2018

My research interests are broadly in the topics of Medical Image Analysis, Imaging Biomarkers, and Radiomics. I am curious about all aspects of image analysis that could be used to further personalise cancer treatment.
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Radiomics and quantitative medical imaging analysis

  • Radiomic techniques transform medical imaging into quantitative, descriptive features.
  • Quantitative features could be used as imaging biomarkers that guide patient treatment decisions.
  • Cancer diagnosis, treatment and monitoring could significantly benefit from radiomic analysis due to the abundance of routine imaging already acquired as part of treatment.

Some examples of potential radiomics applications in cancer management:

  • Diagnosis (e.g. benign or malignancy status)
  • Treatment response (i.e. likelihood a given patient will benefit from a given treatment)
  • Tumour aggression (e.g. chance of relapse or disease progression after treatment)

Quantitative radiomic features are typically aggregated from regions of interest defined in the image. In cancer research, this region is usually a segmentation of the primary tumour.

The features collected broadly describe concepts including tumour shape, intensity statistics, and image texture. Additionally, specially designed filters can emphasise image characteristics prior to feature aggregation.

Radiomics standardisation 

Standardisation and reproducibility poses a significant challenge to the adoption of  radiomics techniques in a clinical setting. Large scale adoption will require appropriate validation of models. 


A key part of my research is in the standardisation of radiomics algorithms and reporting guidelines necessary to replicate studies. To achieve this aim, I am a core member of an international effort (Image Biomarker Standardisation Initiative (IBSI) - https://theibsi.github.io) to standardise image processing through the development of consensus based benchmarks.