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(EPSRC DTP) Machine learning to explore the effect of Sarcopenia for patients undergoing cancer treatments

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  • Full or part time
    Dr A McWilliam
    Dr P Bromiley
    Dr Andrew Green
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Half of people born after 1960 will develop cancer at some point in their lives, the majority will undergo treatment with the intention of curing their disease. Modern cancer treatment takes many forms, including systemic therapies such as chemotherapy and targeted therapies such as radiotherapy. However, many patients are quite unwell as a result of their disease and their health may further deteriorate during treatment. Resultant weight loss, particularly the loss of skeletal muscle, sarcopenia, affects a patient’s ability to withstand treatment and, in chemotherapy, is known to be highly predictive of survival. Such a link is also expected in radiotherapy but has not yet been fully established. Assessment of sarcopenia before and during the care pathway would aid clinicians in selecting appropriate radiotherapy and supportive treatments to maximise quality of life and survival rates. However, current methods for sarcopenia assessment involve identifying a particular abdominal region on CT and manually drawing around the fat and muscle. This is extremely time-consuming and so sarcopenia screening is not currently a part of routine care.

The aim of this project is to develop state-of-the-art machine learning software that can automate the process of sarcopenia assessment. In previous work, we have developed software that can automatically identify the spine in 3D CT images, as part of a screening procedure for vertebral fractures. This method will be adapted to automatically identify the correct region of the abdomen for sarcopenia assessment by labelling particular vertebrae, and then utilizing machine learning to identify the fat and muscle in that region, from which a sarcopenia index will be derived. Evaluation will be performed on a database of several thousand lung cancer patients treated at the Christie Hospital, which is a patient group known to show significant frailties. This data would inform the first large scale analysis of the effect of sarcopenia on radiotherapy outcomes. Additionally, we will investigate changes sarcopenia scores for prostate patients from arm K of the STAMPEDE trial. Here we aim to providing a quantitative measure of the protective effects of metformin on muscle wasting due to hormone therapy.

The outcome will be a software package that clinicians can use to quickly perform sarcopenia assessment, allowing it to be integrated into routine radiotherapy care. The project is interdisciplinary and the student will be supervised by researchers in both the Division of Cancer Sciences and in the Division of Informatics, Imaging and Data Sciences.

Entry Requirements
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
On the online application form select PhD Cancer Sciences.
For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (

Funding Notes

EPSRC DTP studentship with funding for a duration of 3.5 years to commence in September 2019. The studentship covers UK/EU tuition fees and an annual minimum stipend (£15,009 per annum 2019/20). Due to funding restrictions the studentship is open to UK and EU nationals with 3 years residency in the UK.

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.


Mcpartlin, A., Kershaw, L., McWilliam, A., Van Herk, M., Taylor, B., Choudhury, A. “Changes in prostate apparent diffusion coefficient values during radiotherapy after neo-adjuvant hormones”, Therapeutic advances in Urology.
(In press) 2018

Beasley W., Thor M., McWilliam A., Green A., Mackay R., Slevin N., Olsson C., Pettersson N., Finizia C., Estilo C., Riaz N., Lee NY., Deasy JO., van Herk M. “Image-based Data Mining to Probe Dosimetric Correlates of Radiation-induced Trismus.”, Int J Radiat Oncol Biol Phys. S0360-3016(18)30906-4. 2018

McWilliam A., Kennedy J., Hodgson C., Vasquez Osorio E., Faivre-Finn C., van Herk M. “Radiation dose to heart base linked with poorer survival in lung cancer patients.” Eur J Cancer. 85:106-113. 2017

P.A. Bromiley, E.P. Kariki, J.E. Adams and T.F. Cootes. “Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling.” Computational Methods and Clinical Applications in Musculoskeletal Imaging. LNCS vol. 10734. Eds. Ben Glocker, Alejandro Frangi, Hianhua Yao, Guoyan Zheng and Tomaz Vrtovec. Springer International Publishing p. 133-147, 2017.

P.A. Bromiley, E.P. Kariki, J.E. Adams and T.F. Cootes. “Fully Automatic Localisation of Vertebrae in CT images using Random Forest Regression Voting.” Computational Methods and Clinical Applications for Spine Imaging. LNCS vol. 10182. Eds. Jianhua Yao, Tomaz Vrtovec, Guoyan Zheng, Alejandro Frangi, Ben Glocker, and Shuo Li. Springer International Publishing, p. 51-63, 2017.

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