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The outcomes of innovative radiotherapy regimes for the treatment of cancer under the RadNet programme

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  • Full or part time
    Prof G Royle
    Dr C Allemani
    Prof M Coleman
  • 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

Radiotherapy is a key treatment modality for at least half of all cancers. Delivering a curative radiation dose to the cancer can be complicated by imprecise targeting of the tumour, radiation resistance, clonal heterogeneity or other reasons.

RadNet is a major new research programme funded by Cancer Research UK, designed to unlock the full potential of radiation-based cancer treatment. The City of London Radiation Research Unit will combine the academic and clinical resources of three major London universities (UCL, KCL, QMUL), the Francis Crick Institute, Europe’s largest paediatric cancer service, and the UK’s largest Cancer Trials Centre, serving a population of 10 million people.
The Radiation Research Unit will become a UK-wide focus for studying three aspects of radiotherapy: radiation resistance, the optimal combinations of radiotherapy with other treatment modalities, and technologies for more precise targeting of radiation to the tumour, via X-ray, proton beam or targeted radionuclides.

Assessment of outcomes and adverse reactions will be a cross-cutting theme. We will evaluate the approaches to improving treatment efficacy in several ways: novel biomarkers, the Response Evaluation Criteria In Solid Tumours (RECIST) categories, as measured by imaging (complete or partial response, or progressive disease); short-term and long-term survival, and adverse effects.

This doctoral research project involves the design of a comprehensive platform for radiotherapy data, including how to collect, code, curate and analyse individual patient data from NHS Hospital Trusts across north London. The goal will be to develop and apply standardised approaches to interrogate the data in as close to real time as possible, to produce readily interpretable outputs for clinical, public health and research communities, and to guide the choice of treatment regimens for individual patients by enabling the prediction of clinical outcomes.

You will study the frequency of each radiotherapy regime by the type and stage of cancer and the hospital of treatment, as well as by demographic characteristics such as the patient’s age, sex and socio-economic status. You will study the probability that patients have been treated in compliance with applicable clinical guidelines (patterns of care). Outcomes will include the probability of short-term and long-term survival for each type of cancer, by stage at diagnosis, for each main category of radiotherapy. You will document the most frequent short-term and long-term radiation-related complications that may adversely affect the quality of life (survivorship).
You possess a high-standard first degree and a master’s degree in mathematics, biostatistics, epidemiology or a closely related subject, ideally with research or work experience in computing or programming with large databases in cancer, medicine or public health. You will be offered training in a wide range of disciplines, and you will collaborate with oncologists, radiotherapists, physicists, statisticians and public health specialists. Above all, you are enthusiastic about a research career in cancer, dedicated to helping improve outcomes for all cancer patients. For further details on how to apply please visit the CRUK CoL Centre RadNet studentships page:

Potential research placements

1. Basic and advanced training in Stata programming. This course would be supplemented with online courses made available by Stata. Location: LSHTM and on-line.

2. Basic training in radiation physics and radiotherapy techniques. To acquire an understanding of the exposures, parameters, and deployment of radiation in the diagnosis and treatment of malignant disease, and of the available sources of data about radiation machinery and patients treated with radiotherapy, in order to develop an understanding of how data should be collected, coded, curated and analysed in order to assess the outcomes of treatment. Dr Gary Royle, UCLH

3. Training in the principles and methods of cancer survival analysis. Dr Claudia Allemani and Prof Michel Coleman, LSHTM

Funding Notes

Due to funding restrictions only UK / EU candidates are eligible to apply

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