Overview
I joined the Cardiff School of Engineering as an EPSRC and Velindre Cancer Centre funded research student in 2015. After completing my PhD, I joined the Cancer Imaging and Data Analytics research group as a research associate. My research focuses on the segmentation of the tumour on PET imaging, as it has been identified as one of the largest sources of error in radiotherapy treatment planning. My research aims to improve the quality of radiotherapy treatment planning by investigating methods that can improve tumour segmentation.
Biography
Work & Education
- Research Associate, Cardiff University 2019 - Current
- PhD, Cardiff University 2015 - 2019
- Data Interface Developer, Travel Places 2014 - 2015
- MSc Computer Science, Bangor University 2013 - 2014
- BSc Computer Science, Bangor University 2010 - 2013
Academic positions
2013 - 2014: Postgraduate Demonstrator, Bangor University
Speaking engagements
- European Association of Nuclear Medicine, Virtual (2020)
- NCRI Virtual showcase (2020)
- European Association of Nuclear Medicine, Dusseldorf (2018)
- All Wales Medical Physics and Clinical Engineering Summer meeting, Cardiff (2018)
- All Wales Medical Physics and Clinical Engineering Summer meeting, Cardiff (2017)
- All Wales Medical Physics and Clinical Engineering Summer meeting, Cardiff (2016)
Committees and reviewing
- Journal Reviewer, Physics in Medicine & Biology
- Journal Reviewer, European Journal of Nuclear Medicine & Medical Imaging Research
Publications
2021
- Shi, Z. et al. 2021. Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: an external validation study. British Journal of Radiology 94(1118), article number: 20201042. (10.1259/bjr.20201042)
2020
- Spezi, E. et al. 2020. Metabolic tumour volume segmentation for oesophageal cancer on hybrid PET/CT using convolutional network architecture. Presented at: 33rd Annual European Association of Nuclear Medicine Congress (EANM 2020), Virtual, 22-30 October 2020European Journal of Nuclear Medicine and Molecular Imaging, Vol. 47. Vol. S1. Springer Verlag (Germany) pp. 5481-5482., (10.1007/s00259-020-04988-4)
- Parkinson, C. et al. 2020. Qualitative assessment of oesophageal cancer metabolic tumour volumes delineated by an artificial intelligence algorithm. Presented at: NCRI Virtual Showcase 2020, Virtual, 2-3 November 2020.
2019
- Hargreaves, S., Johnstone, E., Parkinson, C., Rackley, T., Spezi, E., Staffurth, J. and Evans, M. 2019. Interim 18F-FDG positron emission tomography/computed tomography during chemoradiotherapy in the management of cancer patients: a response [Letter]. Clinical Oncology 31(9), pp. 669-670. (10.1016/j.clon.2019.05.005)
- Parkinson, C. 2019. Advanced automated PET image segmentation in radiation therapy. PhD Thesis, Cardiff University.
- Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Scientific Reports 9(1), article number: 9649. (10.1038/s41598-019-46030-0)
- Parkinson, C. et al. 2019. Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy. Physica Medica, European Journal of Medical Physics 61, pp. 85-93. (10.1016/j.ejmp.2019.04.020)
- Foley, K. G. et al. 2019. External validation of a prognostic model incorporating quantitative PET image features in esophageal cancer. Radiotherapy and Oncology 133, pp. 205-212. (10.1016/j.radonc.2018.10.033)
- Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. PO-0963 A novel normalisation technique for voxel size dependent radiomic features in oesophageal cancer. Radiotherapy and Oncology 133(S1), pp. S523-S524. (10.1016/S0167-8140(19)31383-0)
- Whybra, P., Parkinson, C., Foley, K., Staffurth, J. and Spezi, E. 2019. A novel normalisation technique for voxel size dependent radiomic features in oesophageal cancer [Abstract]. Radiotherapy and Oncology 133, pp. S523-S524. (10.1016/S0167-8140(19)31383-0)
2018
- Parkinson, C. et al. 2018. Dependency of patient risk stratification on PET target volume definition in oesophageal cancer. Presented at: ESTRO, Barcelona, Spain, 20-24 April 2018.
- Parkinson, C., Marshall, C., Staffurth, J. and Spezi, E. 2018. ATLAAS - Investigation into the Incorporation of Morphological Data on Automated Segmentation [Abstract]. European Journal of Nuclear Medicine and Molecular Imaging 45(S1), pp. S72-S73. (10.1007/s00259-018-4148-3)
- Parkinson, C., Whybra, P., Staffurth, J., Marshall, C. and Spezi, E. 2018. ATLAAS - Investigation into the incorporation of morphological data on automated segmentation. Presented at: EANM Congress 2018: European Association of Nuclear Medicine., Dusseldorf, Germany, 12-18 October 2018.
- Parkinson, C. et al. 2018. Target volume delineation of FDG PET images post one cycle of induction chemotherapy in oropharyngeal cancer using advanced automated segmentation methods. Presented at: ESTRO 37, Barcelona, 20-24 April 2018.
- Parkinson, C. et al. 2018. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods. EJNMMI Research 8, article number: 29. (10.1186/s13550-018-0379-3)
- Whybra, P., Foley, K., Parkinson, C., Staffurth, J. and Spezi, E. 2018. Effect of interpolation on 3D texture analysis of PET imaging in oesophageal cancer. Radiotherapy and Oncology 127(S1), pp. S1164-S1165. (10.1016/S0167-8140(18)32426-5)
- Parkinson, C. et al. 2018. Target volume delineation of PET post one cycle of induction chemotherapy in oropharyngeal cancer. Radiotherapy and Oncology 127(S1), pp. S634-S635., article number: EP-1126. (10.1016/S0167-8140(18)31436-1)
- Parkinson, C. et al. 2018. Dependency of patient risk stratification on PET target volume definition in Oesophageal cancer. Radiotherapy and Oncology 127(S1), pp. S503-S504. (10.1016/S0167-8140(18)31241-6)
- Foley, K. G. et al. 2018. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European Radiology 28, pp. 428-436. (10.1007/s00330-017-4973-y)
2017
- Parkinson, C., Chan, J., Syndikus, I., Marshall, C., Staffurth, J. and Spezi, E. 2017. Impact of 18F-Choline PET scan acquisition time on delineation of GTV in prostate cancer [Poster Abstract]. Radiotherapy and Oncology 123(S1), pp. S714-S715. (10.1016/S0167-8140(17)31768-1)
Teaching
Lecturer on the Medical Engineering (MEng) Medical Image Processing module.
Segmentation in radiotherapy treatment planning
In radiotherapy planning, the segmentation of the tumour has been highlighted as one of the greatest sources of error. This is a manual process, which is not only time extensive but also subject to inter and intraobserver variability. Although automated segmentation algorithms have also been proposed for accurate tumour segmentation, they have limited accuracy in PET scans acquired after chemotherapy and have been shown to be not suitable in all cases. Therefore, there is increasing interest in using machine-learning techniques to improve the segmentation of the tumour. ATLAAS is decision-tree based segmentation methodology that was validated in diagnostic H&N cancer and externally validated in intra-treatment PET scans. It uses estimated tumour characteristics to select one of many segmentation algorithms included in the training dataset. In comparison to segmentation performed on a CT image, PET imaging and the subsequent results of segmentation have poor resolution. Therefore, there is interest in improving the quality of the segmentation. Interpolation of PET imaging before segmentation with ATLAAS reduces the aliasing artefacts and improves tumour segmentation accuracy.
Currently, deep-learning techniques are showing promise for accurate tumour segmentation. However, they have not been thoroughly investigated. My current research is looking at deep-learned segmentation of the tumour in comparison to traditional machine-learning techniques.
Projects
PEARL: Adaptive radiotherapy planning September 2020 - Current
ASPIRE: AI Solutions for Personalised Radiotherapy (Efficiency Through Technology programme) April 2019 - September 2020
TITAN: Advanced Automated Segmentation of PET imaging for use in Radiation Therapy October 2015 - April 2019
Collaborations
MAASTRO, Netherlands 2017 - current
Guy's and St Thomas Hospital, London 2017 - current
Velindre Cancer Centre, Cardiff 2015 - current