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Novel imaging device enters first round of development funding programme

Proton-beam-therapy (PBT) is becoming increasingly important for treating cancer, with projected increases of up to 50% more patients per year being treated with the technology in the UK and worldwide by 2025.

Although the precision of PBT has many advantages over traditional radiotherapy, there some uncertainty over the range of delivery the beam provides. There is risk of potential overdose to normal tissues or underdose to tumour, resulting in reduced tumour-control and long-term side-effects due to treatment of healthy tissue. This can be detrimental to patients and a burden on healthcare systems if side-effects become apparent later in a patient’s life.

Therefore, a method to verify the range of treatment beams when using PBT on patients is crucial to increase the treatment accuracy. Dr Anna Vella, Postdoctoral with the Radiation Therapy Medical Physics Group, led by Prof. Frank Van Den Heuvel, at the University of Oxford’s Department of Oncology, is investigating the efficacy of a device with this purpose.

Anna is leading CAPULET (Coded Aperture Prompt-gamma Ultra-Light imaging detector), an imaging device for quality assurance assessment of radiotherapy plan efficacy, designed for daily use in clinical practice. CAPULET could be installed onto a variety of PBT devices, and used to verify and fine-tune the dose between fractions in particle-beam radiotherapy. It does this through collecting 3D images of the particle beam penetrating soft-tissue, with the ultimate goal to fine-tune planning doses and improving the efficacy of the overall radiotherapy treatment.

This novel and unique technology is faster & more compact than current devices, increases the field-of-view, and improves the signal-to-noise ratio. The impact on patients will be to improve cancer-control, fewer complications, and improved quality-of-life following treatment.

CAPULET has recently been selected as one of 35 projects in the Pre-Development Phase of the Alderley Park Oncology Development Programme – a national programme designed to develop and progress start-up oncology projects. Funded by Innovate UK and Cancer Research UK. It will now be work-shopped, and potentially be chosen to join the full development programme with grant funding.

Proof-of-concept experiments will be performed in collaboration with the CRUK-funded ART-NET. The long-term plan of CAPULET is to develop a large-area detector to fully image the beam delivery range within lungs, liver, H&N and other large sites in the human body to overcome limited field-of-view found in other existing devices on the market.

Using big data in breast cancer research

Breast cancer is the most common type of cancer found in the UK population, with 1 in 8 women diagnosed during their lifetime. As a prevalent cancer, it’s important to understand more about the potential causes and relative risks that individuals from different demographics might have.

The Cancer Epidemiology Unit (CEU) at the Nuffield Department of Population Health specialises in large-scale studies into the lifestyle and genetic risk factors of cancers such as breast cancer. In doing so, these studies can provide evidence to inform public health policies and answer outstanding questions about how cancers may arise.

The cause of breast cancer has long been researched and over the last two decades, findings from the Unit’s large prospective studies and international collaborations have helped clarify the role of many risk factors for the disease, including use of menopausal hormones and oral contraceptives, as well as factors relating to childbearing. Within the last year, an updated review of the worldwide evidence carried out in CEU showed that menopausal hormone usage increases the long term risk of breast cancer by almost twice as much as was previously thought, findings which influenced public health guidance. Other recent work found evidence to suggest that high fruit and fibre intake and physical activity may be associated with lower risks of breast cancer.

The CEU work with big data, such as its Million Women Study which contains data from 1.3 million UK women, collected since it began  in 1996. The study includes 1 in 4 of all UK women born between 1935 and 1950 and remains the largest data set of its kind. The study, which aims to resurvey women every 3-5 years, continues to collect information on new potential risk factors such as working night-shifts (which in this case was shown to have no influence on breast cancer incidence).

Enhancing the quality and quantity of the Million Women Study dataset is high on the CEU’s agenda. One area of research where the study hopes to be able to contribute substantially over the next few years is in risk stratification for breast cancer. Prediction models which can be used to assess an individual’s breast cancer risk are key for planning risk-based screening approaches that are tailored to an individual, so refining their accuracy is important to ensure that interventions can be targeted appropriately. However, while existing risk prediction models look promising they need further improvement in their ability to identify those women who are most likely to get breast cancer before they can be applied at a population level. In particular, models should ideally incorporate the whole spectrum of breast cancer risk factors including genetic variation and radiological imaging data.

This will be the next stage for the Million Women Study, as Prof Gill Reeves, Head of the CEU, hopes to integrate new datasets into the study. This includes digital screening images from mammograms, and other clinical information, which could be used in combination with existing information held on participants, to allow the CEU to develop more accurate risk prediction models from the Million Women’s Study.

Prof Gill Reeves, Head of the CEU, says:

“Enriching the quality of datasets such as the Million Women Study will allow us to continue to provide reliable evidence regarding the effects of behavioural and biological factors on breast cancer risk, and help identify women who are at particularly high risk of the disease. In doing so, we can better inform public health advice, and clinical practice.”

To read more about the CEU’s work on breast cancer go to the  CEU website.

About the CEU

The CEU runs the Million Women’s Study (MWS) and EPIC-Oxford (two large cohort studies). Recent grant funding from CRUK has allowed for further enhancement of the MWS so that new clinical data and other potential risk factors for cancer may be integrated.

Prof Gill Reeves, Head of the CEU, is a Professor of Statistical Epidemiology. Her main research interests are the roles of hormonal and other risk factors in the development of female cancers.  She is particularly interested in risk factors and patterns of survival for molecular subtypes of breast cancer.

 

A new FRONTIER for breast cancer

Latest news from FRONTIER, the trial investigating the potential of the radiotracer Fluciclovine in the subtyping and staging of breast cancers

NCITA: a new consortium on cancer imaging

Cancer imaging is an umbrella term that defines diagnostic procedures to identify cancer through imaging – such as scans via x-rays, CT scans and ultrasounds. There is no single imaging test that can accurately diagnose cancer, but a variety of imaging tests can be used in the monitoring of cancer and planning of its treatments.

What is NCITA?

NCITA – the UK National Cancer Imaging Translational Accelerator – is a new consortium that brings together world leading medical imaging experts to create an infrastructure for standardising the cancer imaging process, in order to improve its application in clinical cancer treatment.

Research and medical experts from the University of Oxford have come together with UCL, University of Manchester, the Institute of Cancer Research, Imperial, Cambridge University and many more to create this open access platform.

How will NCITA help cancer research?

On top of bringing together leading experts in cancer imaging to share their knowledge, the NCITA consortium will create a variety of systems, software and facilities to help localise and distribute new research and create a centralised location for cancer-image data to be analysed.

NCITA will in include a data repository for imaging, artificial intelligence (AI) tools and training opportunities – all of which will contributing to a revolution in the speed and accuracy of cancer diagnosis, tumour classification and patient response to treatment.

The NCITA network is led by Prof Shonit Punwani, Prof James O’Connor, Prof Eric Aboagye, Prof Geoff Higgins, Prof Evis Sala, Prof Dow Mu Koh, Prof Tony Ng, Prof Hing Leung and Prof Ruth Plummer with up to 49 co-investigators supporting the NCITA initiative.  NCITA is keen to expand and bring in new academic and industrial partnerships as it develops.

Go to the NCITA website to stay up to date of news about cancer imaging research.

For more information on this exciting new initiative, see the media release about the NCITA launch here.

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