Blog

Blog 8 - Cosmology & Cancer

Dr James Nightingale, Durham University

'At their core, both problems seek to overcome the same challenge: multi-scale complexity'


For both cosmology and cancer research the challenge faced utilizing big data is fundamentally the same. The scientist has numerous observations, which each provide a narrow snapshot into a specific aspect of the overall system. Their goal is to extract meaningful information from an extremely large dataset, using robust and reliable statistical techniques.

For example, in my Cosmology research, I analyse images of tens of thousands of galaxies. Each image contains an extremely small amount of information about the dark matter contents of the region of the Universe that the galaxy resides. However, extracting this signal requires that I separate it from the intrinsic properties of the galaxy itself, necessitating detailed modeling and analysis of every individual dataset.

Healthcare researchers working in big data are faced with a similar task, albeit their data is now medical in nature and they wish to separate out what each observation tells them about the treatment of individual patients from global trends in patient outcomes. However, the statistical methods underpinning their analysis are the same ones I use to study dark matter, they are simply being applied in another scientific domain and to a completely different problem!

A couple of years ago, I by chance had lunch with the CTO of Concr, a Biotech company who make predictive models of how cancer therapy may work. We got chatting about statistics, Bayesian inference, model-fitting -- and quickly realised we had a lot more in common than we initially expected!

We began working together on a project we were soon referring to as “Cosmology & Cancer”, with our aim to collaborate on the development of statistical methods that could overcome the challenges we both faced in the big data era. With the healthcare company Roche and the Christie NHS Foundation Trust we are now working on an Innovate UK clinical trial. This is centred around Carcinoma of Unknown Primary (CUP) Site, where malignant cancer cells are found in a patient but the original cancer is unknown. This makes it extremely challenging to treat, withcurrently no approved therapies or immunotherapies available in the UK.

With Concr, I oversee development of a multi-level modeling framework to fit their models of cancer to clinical data on CUP patients. This centres around the shared development of open-source statistics software titled PyAutoFit (https://github.com/rhayes777/PyAutoFit), which hosts the inference techniques we both need to analyse large datasets.

For CUP diagnosis, we are building multi-level graphical models of cancer. These models aim to properly account for multi-scale complexity, by hierarchically fitting datasets that provide information at all relevant physical scales. The inner levels comprise data on the cellular and molecular scales (e.g. genetic or epigenetic profiles) to infer how different cancer cells respond to treatments. Higher levels combine these models to represent the evolution and dynamics of tumours, whilst the highest levels map out effective treatment paths using data on patient outcomes.

By comparing these results to known data of a CUP patient, the hope is to build a predictive model that can determine the original cancer and provide an evidence-based pathway for treatment.

The project has now come full circle, with this framework finding its way into my Cosmology research. The inner levels are now models on the scale of individual galaxies, whilst the higher levels describe perhaps the largest scale conceivable -- that of the entire observable Universe. Remarkably, code that someone else wrote to improve cancer therapy is helping me better understand our Universe!

When we set out on this project, our goal was to simply share code for analysing large datasets. However, as I become more immersed in biological research, I am starting to question if there is a more fundamental link with Cosmology. At their core, both problems seek to overcome the same challenge: multi-scale complexity. As I study them further, symmetries are emerging in my understanding of both problems, and it is uncovering whether this link is more than coincidental that motivates me to pursue this research further.

However, it is in my personal and professional development level this project has had the most profound impact. I have been taken aback by how much I have learned working with those in other scientific disciplines. It has been an eye-opening and incredibly positive experience from which I have learnt numerous statistical techniques, software development practises and approaches to “doing science” that I was simply not exposed to in Astronomy. Adopting these has furthered my own cosmology research and grown me as a scientist, and I am passionate to share my experience with the wider field of Astronomy.

Oakley Clark, May 2021

Blog 8 - Meet the Scientist, Oakley Clark discusses his scientific interests and aspirations for his project

Oakley Clark, University of Surrey, May 2021

What are your main research interests?

My research relies on X-ray spectroscopy, and so one of my main interests is to improve analysis by making corrections to the spectra based on the underlying physics. A lot of this work is based upon theory and modelling the problems with computer simulations, and I enjoy this challenge. It is great however that I get to combine this with taking my own measurements in the lab. In the future, I look forward to being able to test some of these algorithms by applying them to measurements of some phantoms.

What is the focus of your research?

The goal of my research is to create a method to measure the density of breast tissue. Breast tissue can be broken down into adipose and glandular tissues, the proportions of which govern its density. By examining the exposure of such a tissue to X-rays, it is possible to reconstruct how much of each constituent tissue there is, and therefore calculate the density.

What are the potential impacts of this research?

Studies have found a link between breast density and breast cancer risk, so such a measurement could form part of the screening process to improve its efficiency.

What made you decide on a career in physics?

At each educational stage, I’ve always picked the subjects that I enjoy the most, which led me to studying a degree in physics. I chose to go into research over other routes because I love the idea of finding some new information that hasn’t been uncovered before. Not every discovery can be ground-breaking, but it would be amazing if I could expand human knowledge by a tiny amount.

What has been the highlight of your research career so far?

So far it has been my transition into working in the lab. I love the area of physics I’m working in, but previously I did not have any practical experience of taking my own measurements using X-rays. I’ve really enjoyed having the opportunity to apply the theory within the lab.

What advice would you give to someone considering a career in scientific research? What are the highs and lows so far?

There will be times you explore an alley that later closes, and I think it’s important not to be put off by this. I think this is outweighed by the freedom to explore an area you really enjoy. For anyone who is considering a career in research, I would say go for it!

Prof Bogdan Matuszewski, Feb 2021

Blog 7 - Machine Learning System for Decision Support and Computational Automation of Early Cancer Detection and Categorisation in Colonoscopy

Written by Prof Bogdan Matuszewski, University of Central Lancashire, Feb 2021

“Improvement of polyp detection rate by 1% is estimated to reduces the risk of colorectal cancer by 3%. With the help of new machine learning methodologies, it seems conceivable to significantly increase robustness and effectiveness of colorectal cancer screening improving polyp detection rate.”


STFC CDN+ is supporting a project aiming to develop new computational methods to improve robustness and effectiveness of colorectal cancer screening, improving lesion detectability and provide endoscopists with practical navigation tools, in effect reducing cost, risk and discomfort to patients.

Colorectal cancer (CRC) is one of the leading causes of cancer deaths worldwide, with the survival rate depending strongly on an early detection, hence the importance of the colon screening. It is commonly accepted that most colorectal cancers evolve from precursor adenomatous polyps. Typically, a colonoscopy screening is proposed to detect polyps before any malignant transformation, or at an early cancer stage. The optical colonoscopy is the gold standard for colon screening due to its ability of detecting and treating the lesions during the same procedure. However, colonoscopy screening has some limitations.

Various recent studies have reported that about a quarter of colon polyps could be missed during routine colonoscopy screening procedures, with a sizable number of patients having at least one polyp missed. It has been also estimated that improvement of polyp detection rate by 1% reduces the risk of CRC by 3%. It is therefore essential to improve polyp detectability as it can reduce the risk of colorectal cancer and healthcare costs. Equally, correct classification of detected polyps is limited by polyp appearance and subjectivity of the assessment.

“My work on the analysis of video colonoscopy data started in 2015 with participation in the Endoscopic Vision Challenge organised as part of the Medical Image Computing and Computer Assisted Interventions (MICCAI) conference. This was done following EPSRC-funded studies that I had been conducting since 2007, in collaboration with medical physicists and optical engineers, and focused on the applications of computer vision in radiotherapy. Teams from my Computer Vision and Machine Learning (CVML) research group continued to participate in the subsequent Endoscopic Vision Challenges, which we won twice. Through these years we have built up an expertise in this subject area, reflected by the successful completion of a PhD project, and managed to establish collaborative links with UK and EU academic and clinical partners. The STFC CND+ proof of concept funding is facilitating collaboration with computational physicists to further optimise, develop and integrate existing solution to a stage that it can be independently assessed by our clinical partners, with a view of taking the created software to a Technology Readiness Level (TRL) suitable for clinical study/trial”

The key challenges and expected project innovations are to advance the current state-of-the art in deep machine learning applied to colonoscopy data. The focus is on developing techniques for automatic detection, segmentation and categorisation of polyps, aiming to reduce chances of polyps being missed during colonoscopy procedures. Furthermore, we aim to detect image artefacts (e.g. saturation, low contrast areas, blur or specularity) and foreign objects to support endoscopists in interpreting the data during examination. We are also looking at developing visual aids supporting colon examination. These will include 3D structure visualisation as well as navigation tools based on visual odometry techniques.

The key expected output of the project is a set of software tools aimed at improving polyp detectability and therefore ultimately leading to a reduced risk of colorectal cancer. The developed software is to be tested and evaluated by clinical endoscopists, preparing grounds for future clinical studies validating the methodology in a hospital environment.

To take the full advantage of the available resources and the research effort, we will also consider secondary application areas to fully utilise the resources and the project outcomes. We intend to test the developed machine learning methods on different medical applications. In particular, through transfer learning, we expect to validate developed machine learning solutions on a radiomics problem, quantifying CT scan density heterogeneity in renal cancer.

Building on the existing base of UK and EU collaborators, it is expected that the project will lead to creation of a strong multifaceted international consortium capable of developing, clinically validating, and deploying automated colonoscopy systems to clinical practice. It is envisaged that the project will be instrumental in building such a consortium, which would subsequently apply for funding to support further research and secure the transfer of the developed technology to a higher Technology Readiness Level.

“An important motivation for my work, including this project, is to see how state-of-the-art computational methods can be translated into tools that can have a real impact on the health and quality of life for us all.”

Blog 6 - Early Diagnosis Virtual Event

Written by James Ingham, University of Liverpool, Nov 2020

It was a pleasure to attend the STFC cancer diagnosis network event, which turned out to be a fantastic melting pot of both research and commercial groups from a variety of backgrounds and interests, including the day to day medical diagnostics/treatments and also new and emerging technological fields. Coming from a physics (instrumentation) background I see events such as this as a crucial opportunity to bring the people from the front lines of treating patients and people developing the emerging technologies together. At Sciascan we are developing a probe for the rapid detection of cancer, we do this by implementing a variety of technological and machine learning advancements. Events such as this are critical for us to engage with the medical community to ensure the probe is designed in a robust and easily accessible package which meet the needs of the medics.

The event kicked off with a great set of talks from medical experts in a range of settings, from the GP surgery to the operating theatre. As each talk had a slightly different perspective on the current ‘needs’ for cancer diagnostics it meant each talk took a different approach and would likely appeal to a wide range of technologies which was brilliant. The talks were followed by a panel section allowing for great interaction and a review of the current funding landscape by both STFC and CRUK.

After a small break we entered into the breakout portion of the event where we were divided into groups of five or so with a range of expertise and tasked to give input on relevant questions form the previous panel section. We then reviewed the questions/answers among all the attendees where we could share our insights. I found this to be a very interesting and informative part of the event.

The event was then finished with a section where the attending commercial groups were placed into individual breakout rooms and a ‘speed dating’ style format was used where interested researchers/medical professionals could pick and choose which groups they wanted to talk to further. I personally found this section to be a great experience as the people who had an overlapping area of interest were able to have an informal back and forth chat.

Overall, the event was a great example of bringing people together from different backgrounds to share ideas and bring a range of insights to the common goal of cancer diagnosis.

Dr James Ingham, SciaScan, Nov 2020
SciaScan was set up by the University of Liverpool to develop instrumentation for cancer diagnosis. It was founded on the expertise of the physicists and medics working at the University who developed the necessary hardware and software.

Blog 5 - Bringing the voices of patients and clinicians into research - Sept 2020, Tracy O'Regan

Written by Caroline Wood

“It doesn’t matter how amazing a new technology is, if it can’t work within, improve, or develop the healthcare system then it will never be used.” Dr Tracy O’Regan is making sure that the voices of patients and clinicians are heard within the STFC Cancer Diagnosis Network (CDN).

The ultimate aim for all cancer research is to improve patient outcomes. But new diagnosis methods and therapies will only be beneficial if they can be used by front-line staff within existing or emerging clinical settings and pathways. To achieve this, Dr Tracy O’Regan, an officer for The Society and College of Radiographers (SCoR), wants to encourage greater engagement between STFC CDN and clinicians and patients. “This isn’t about stifling innovation or quashing new ideas” she says. “The purpose is to make sure that new technologies really do address the needs within the healthcare system.”

Having worked as a diagnostic radiographer in a variety of clinical settings – from outpatient care to A&E - Tracy appreciates first-hand how much care pathways can vary from one sector to another. Adding to this complexity, an increasing number of the patients within our ageing societies have multiple diseases or long-term conditions. Yet there can be huge differences between the various departments required to fully address the needs of local populations, from the format of appointments to technological gulfs. “Radiographers, for instance, have always been fully embedded in technology, whereas other departments still use hand written notes” Tracy says. She argues that this complexity within clinical care needs to be appreciated when new treatments and diagnostic tools are developed, with innovators working with clinicians from the planning phase and not just at the point of implementation. “What is really needed is a demand-signalling approach where researchers work closely with clinicians, patients and carers, to understand the issues and bottlenecks in current and evolving practice so that they can purposefully address these” she says.

Recent years have also seen the voice of the patient brought to the centre of healthcare. “Health systems used to have a very patriarchal approach, where the doctor knew best and the patient’s role was to simply follow their advice. But now we recognise that patients are experts by experience, which has led to the concept of ‘person-centred’ care” Tracy says. Increasingly, new technologies are being developed using a co-creation approach, where innovators work with patients throughout the process to deliver solutions that both achieve health benefits and are well tolerated. For example, the Patient Advisory Group for SCoR provides input on a range of the society’s activities, including their publications, consultation responses and research agenda.

Whilst she hopes that similar groups could help STFC CDN access patient and carer perspectives, she recognises that relying on volunteers has its limitations: “These groups can have a certain bias because it tends to be a particular type of person that will step forward, for instance in response to a poster in a waiting room inviting them to take part. Certain demographic groups, or patients who have experienced difficult treatments, may be more hesitant to engage in this way.” Nevertheless, it is still crucial to capture as many viewpoints as possible, as Tracy explains: “It is important to recognise that you cannot simply group people together: different cancers and treatments have very distinct effects and even patients with the same type of cancer will have different experiences depending on their individual values, overall health and preferences.” Fortunately, with a little creativity, there are many ways in which healthcare professionals, academics and researchers can work with patients and the public in partnership. “The best approach is to reach people where they feel comfortable in engaging, whether this is through community groups, working men’s clubs, faith groups or online support groups” Tracy says.

Understanding the views of patients and clinicians will become even more important as the health service responds to the colossal challenges facing it, from widespread obesity to an impending dementia epidemic. The COVID-19 pandemic has highlighted the pressures already apparent within the NHS and the demand for technologies that can increase capacity and speed up processes – particularly for medical imaging and radiotherapy. “During the acute phase of the pandemic, many NHS and private provider diagnostic radiographers were redeployed, for example to focus on chest imaging for suspected COVID-19 patients” Tracy says. Consequently, screening services for cancers, imaging for long-term conditions such as arthritis and osteoporosis, or acute services, for example, musculoskeletal trauma, virtually came to a standstill. Even as these services slowly reopen, their capacity has been drastically reduced due to more stringent requirements for social distancing, cleaning and disinfection. “We are going to see a massive backlog as a result of the lockdown and there is a real worry that cancer diagnoses will be missed or people will be presenting at a later stage of disease than usual.” Ultimately, it highlights the need for technologies to increase capacity and innovations to enable new ways of working. Tracy cites the example of ‘drive through’ imaging, where patients wait in their cars until summoned by a text message to the room where the scan will be taken, meaning that services are not restricted by the finite capacity of traditional waiting rooms.

“It’s more important than ever for research, development and innovation to be in partnership alongside healthcare staff and service users, with the intention of improving the diagnosis, treatment, quality and experiences of services for people” Tracy concludes.

Tracy O'Regan, SCoR Professional Officer
SCoR World Radiography Day poster, 2020
The Society of Radiographers, www.sor.org

Blog 4 From particle physics to PET scans: making cancer diagnosis cheaper, faster and safer - August 2020, Joshua Porter

Written by Caroline Wood

“What excites me most about this work is the possibility of making it cheaper and easier to detect cancer and save lives – that is a big motivator for me” Josh Porter

A project supported by STFC CDN is applying a new particle physics technique towards making PET scanners cheaper, safer and more accurate for cancer diagnosis.

Accurate diagnosis is perhaps the most critical point of a cancer patient’s treatment journey. But it can also be a bottleneck within healthcare systems: the machines are expensive, the procedures can be lengthy and there are often risks associated with exposure to high-power radiation. A new technology supported by the STFC Cancer Diagnosis Network (STFC CDN) could help overcome these challenges – inspired by one of the world’s most ambitious particle physics experiments.

Positron emission tomography (PET) scanning is a standard procedure for locating tumours and diagnosing cancer. Patients are injected with a mildly radioactive substance which becomes concentrated in cancerous cells. The emitted radiation is detected by an external sensor, which converts the signals into the scan image. The problem, as particle physicist Josh Porter (University of Sussex) explains, is that the detectors are made of ‘absurdly expensive’ crystals, which contribute significantly to the multimillion-pound price tag of a PET scanner. “It’s difficult to replace this material because you must find something that can as accurately pinpoint where the radiation is coming from, and at what energy, in order to picture the tumour” he says. As it happens, developing a new way to accurately measure radiation was a challenge Josh was already wrestling with before he became involved with STFC CDN, although originally for a very different purpose.

“My work with the STFC CDN came about through a particle physics project to develop a new technology for neutrino detection, specifically for the Deep Underground Neutrino Experiment (DUNE)” says Josh. DUNE is an international collaboration studying neutrinos, which are little-understood subatomic particles that could hold the answer to some of the most fundamental questions in the universe, including why the universe is made of matter rather than antimatter. Neutrinos only feebly interact with matter and can travel galactic distances without interference. Consequently, enormous and exquisitely sensitive detectors are required to see them. “Many neutrino detectors use a transparent liquid that acts as a ‘scintillator’” Josh explains. “If a neutrino hits this, a flash of light is emitted and this is detected by sensors around the edge. By measuring the pattern and amount of energy in the flash of light, we can learn something about what type of particle caused the radiation. This is challenging because light is very fast! It is like trying to figure out how far away lightning is by counting how long it takes to hear the thunder.” Last summer, as part of a project between his Masters and PhD courses, Josh developed simulations for an alternative approach called ‘LiquidO’. In this method, the liquid scintillator is infused with paraffin wax, to give a cloudy, opaque substance. This confines the emitted light to a much smaller area than a clear liquid would, allowing the radiation produced to be reconstructed much more precisely.

During his project, Josh attended a conference and it was over the evening meal discussions that he was first introduced to the idea of the LiquidO approach in PET scanners. “If we can replace the crystals in the scanners, this could reduce the price of the machine significantly” he says. “It is also highly likely that it would achieve much better sensitivity and thus more informative scan images.” A scoping grant from STFC CDN allowed Josh to dedicate time to modifying simulations for use in a PET scanner. This included identifying candidate materials that would be dense enough to stop the radiation more quickly. “It’s very important that the radiation emitted from the patient is stopped by the detector and deposits all of its energy there” he explains. “If the radiation scatters, it loses energy and can appear as though it is originating from somewhere else, meaning that any tumours won’t be precisely located.” The results of this work helped obtain follow-on funding from the STFC to develop the technology alongside his PhD studies. “The next stage will be to optimise the algorithms that generate the image from the timing information of the emitted radiation” he says. “We then hope to simulate a patient inside a PET scanner to model what the clinician would see from the scan image.”

It’s clear that STFC CDN has shaped Josh’s career ambitions by opening up an opportunity he hadn’t previously known existed. “I am committed to following this idea through to it being applied on patients” he says. “Ultimately, this could result in cheaper PET scanners so that more hospitals can purchase them and more tests can be carried out. We also expect that our method will be more efficient, so that the patient won’t need to be injected with as much radiation. This could allow a more relaxed approach towards prescribing PET scans in children, since the risk from radiation exposure would be lower.”

“I love physics but also the idea of being able to help people directly. Being part of STFC CDN has given me the opportunity to combine these two passions” he concludes.

Joshua Porter, August 2020
As a caption: Positron annihilation in a simulated version of the new PET scanner technology. Two gamma particles emitted back-to-back hit create balls of light in opposite points of the detector, Joshua Porter, August 2020

Blog 3 Istituto Nazionale di Fisica Nucleare, Umbria, Italy - Feb 2020, Alice Porter

My research focuses on the development of 3D diamond detectors for particle tracking and dosimetry. These are detectors with graphite column electrodes inscribed within a diamond as opposed to the conventional metal plates either side of the sensitive volume. In February 2020, I went on academic exchange to INFN Perugia, with the aim of performing the initial tests of a new 3D diamond prototype. The prototype has surface graphitisation to replace the metal layer that is conventionally fabricated on the top surface of a diamond sensor, to make electrical connection to the readout electronics. This layer changes the behaviour of the electric field and impacts the appropriate applied voltage to operate the detector efficiently. The detector was designed with the focus of radiation dosimetry in the clinical setting. Therefore, a low operation voltage and minimised metallic components in the interest of patient safety albeit high enough for precise dose measurements.

During the placement, I conducted characterisation tests using a lab-based x-ray tube. These tests were to understand the detector response at different voltages to varying beam currents (a proxy for dose rate) before exposing it to clinical dose ranges. At the end of the placement, the group and I visited Careggi University Hospital in Florence. We then could test the detector’s response to clinical dose rates provided by the treatment LINAC. The fast response of 3D diamond detectors allows the possibility to resolve individual LINAC pulses at low pulse frequencies, which is of high interest to the field. Using the detector, we can characterise the LINAC pulses with high temporal and energy resolution. We mounted the detector on top of a moveable robotic stage to take measurements of the LINAC’s beam profile. This is done by taking repeated measurements of the beam but taking a small step in one direction between each exposure. These measurements can help us to understand the spatial resolution of the detector, which is important for measuring the absorbed dose during treatment. Upon finishing the placement, I have a large dataset to analyse and quantify the sensor behaviour.

It was a fantastic opportunity to be focused on practical laboratory testing environment and gain direct experience with experts of using diamond detectors for medicine. I really enjoyed my first time performing beam tests in a real hospital treatment room. Working on a cutting-edge device and showing promising results in real time is exciting, and I am full of motivation to conduct a thorough analysis. Amongst enjoying the Italian food (Perugia is famous for its chocolates) and vistas, it was incredibly valuable to discuss the results and experimental approach and technical problem solving with my colleagues in Perugia. I look forward to the continued collaboration with INFN Perugia during and beyond the data analysis and to discovering full the impact of the findings, hopefully in my first publication as corresponding author!

The detector prototype being aligned with the LINAC laser beam at Careggi University Hospital. Alice Porter, Feb 2020
The Fontana Maggiore in the centre of the Historic Centre of Perugia. Alice Porter, Feb 2020

Blog 2 Challenge Workshop 1: Precision and Quantitative Imaging - 20th Jan 2020, Hannah Brown

My PhD research will contribute towards the development of a CZT detector system for application in Low Dose Molecular Breast Imaging. Therefore, the workshop was a great opportunity to attend talks exploring different Precise and Quantitative Imaging techniques currently being developed for cancer diagnosis. From PET to nanophotonics, the diverse expertise and approaches to diagnosis allowed for a range of interesting perspectives and ideas to be shared.

Facilitated breakout sessions were organised as to group participants according to research interests. These networking groups were incredibly beneficial as they encouraged open discussions surrounding shared interests and challenges. On a broader scale, the knowledge exchange between experts from different working sectors is crucial to answer questions such as ‘what do hospitals need?’ and ‘what can Physicists provide?’

On the whole, the workshop was a great opportunity to get a feel for the ongoing research into cancer diagnosis, in particular for me the different approaches to imaging mammographically dense breasts. The network is a great opportunity to share ideas, offer fresh perspectives and build a community with a common aim of working together to continue to improve means of cancer diagnosis.

Blog contributer - Hannah Brown, PhD student. Feb 2020

Prof Nigel Allinson talking about 'Proton CT in Proton Beam Therapy: Getting New Technology into the Clinic' at the Leicester event. January 2020
Facilitated breakout sessions at the Workshop Challenge, Leicester. January 2020

Blog 1 Launch Event in Liverpool - 9th September 2019, Sarah Bugby

The STFC Cancer Diagnosis Network+ was officially launched on 9th September 2019. More than 90 delegates attended the launch event at the University of Liverpool with attendees from universities, STFC facilities, industry, and healthcare.

The day kicked off with an introduction to the network from Dr Laura Harkness Brennan (University of Liverpool), including the training and funding opportunities available.

We then heard overview talks on each of the network’s key challenge themes. Multimodal Techniques were introduced by Prof Paul Marsden (Kings College London and Guy’s and St Thomas’) who discussed the breadth of imaging technologies currently in use for cancer diagnosis. Dr Ramona Woitek (University of Cambridge) discussed Precision and Quantitative Imaging, and introduced the audience to the varied field of radiomics. Prof Nandita de Souza (Institute of Cancer Research and Royal Marsden Hospital) drilled in to one of the key challenges of Early Diagnosis – we don’t know what it is that we’re not looking at – and provided an overview of the clinical need and challenges. The final challenge theme – Data Science applied to Imaging – was introduced by Dr Mathieu Hatt (French National Institute of Health and Medical Research) who covered the vast potential (and potential pitfalls) of applying machine learning techniques to clinical images.

After lunch, focus shifted to the STFC capabilities which could be applied to these challenge themes, with Dr Andrew Boston (University of Liverpool) providing a broad overview of all aspects of the STFC’s remit. This was followed by case studies showing how STFC research has been applied to medicine, ranging from Dr Martyn Winn’s (STFC Scientific Computing) work on analysing genomic data to the application of quantum entanglement to PET imaging by Prof Daniel Watts (University of York). The series of talks were rounded out by Dr Marlies Goorden (Delft University of Technology) who discussed how technology can be progressed from a lab to the clinic.

The event ended with refreshments and a chance for networking between attendees. Overall, the launch event was a great success and the Cancer Diagnosis Network+ is starting out with more than 50 members, and will only grow from here. After this broad introductory event, future events will be targeted to individual challenge themes, so keep an eye out for events in your area.

Blog contributer - Dr Sarah Bugby, Oct 2019




Members of the STFC CDN+ leadership team at the Launch Event, Liverpool. Sept 2019
Prof Paul Marsden from King’s College London talking about how multimodal techniques are used in cancer diagnosis at the Launch Event, Liverpool. Sept 2019
SciaScan was set up by the University of Liverpool to develop instrumentation for cancer diagnosis. It was founded on the expertise of the physicists and medics working at the University who developed the necessary hardware and software.