Category Archives: Cancer

Radiography, Research, and You

Kim Mason head and shoulders image
Kim Mason

Kim Mason, an Audit and Research Radiographer for Mid Yorkshire Teaching Hospitals Trust, talks about their role as well as the value of radiographer engagement in research activities and how to get involved.

In December, 1895, Wilhelm Röntgen would x-ray the hand of Anna Bertha Ludwig, his wife, using a photographic plate. The new discovery lit a fire in the scientific community, and was so sensational that in the following year over 1,000 articles would be published on the topic of X-rays. Over the next 130 years, medical imaging has undergone many varied evolutions to become a cornerstone of modern-day medicine. All beginning with that first piece of research.

Radiography has exploded into a variety of modalities and specialisms from CT to Ultrasound to MRI; all driven by research and development. That is where I come in.

Hi, I’m Kim and I am an alternative-styled, funky-haired, septum-pierced, disabled Audit and Research Radiographer. That is to say, I’m fairly easy to pick out of a line up. I’m also passionate about education, research, and (of course) radiography. This, I’ve been told, is also very easy to pick up on.

I graduated with my radiography degree in 2018 and since then I’ve worked in a wide variety of departments, from plain film to nuclear medicine. Since I found something to love in every modality it never really mattered which one I was in. Prior to my current position, I’ve spent time as a Radiation Protection Supervisor, and well as a trainer for graduate and post graduate radiographers. Now I’m in Audit and Research, which is far different to anything I’ve done before.

So, what is an Audit and Research Radiographer?

Kim sat in their wheelchair at the UKIO 2023 conference

My position is entirely based in research so I do not currently undertake any non-research imaging. However, I’m not entirely non-clinical. I get trained to undertake research scans on new equipment as required for me to carry out the necessary imaging for research trials. I also provide support to all trials requiring access to our radiography department, regardless of the imaging modality.

My job requires that I read a lot of trial protocols so that I can determine whether we have the necessary resources to undertake the trial. It can be hard work, and for me to be effective I have to have a good understanding of how each of our trusts’ imaging modalities function. I also need to have strong communication skills, especially in regards to explaining radiography perspectives to multi-disciplinary team members who may have little to no understanding of imaging. More than this, I need to be able to form and maintain good relations with all sides to ensure that effective communication can take place.

The work I do allows for better outcomes between radiology and research departments. It also affords me the opportunity to be a part of progress, seeking out better practice or improved technology for the future of imaging. In addition, I am able to undertake my own research, working towards my goals of further academic qualifications.

My role is important. I feel that I and my work contributions are valued by both the radiology and research teams. My pathway for personal development is clear and I am able to see the benefits in the set-up and management of the trials my Trust undertakes.

What is Radiography research and why is it important?

There is often research aimed at improving and advancing the field of diagnostic imaging. Currently there are trials into new scanning techniques; new equipment with the potentials for dose reduction and/or improved image quality and patient experiences; and the use of AI in imaging and reporting.

The benefits of improvements in our field are numerous. Every radiographer wants to give patients the best experience they can, however this is often at odds with the nature of our job. Requiring patients to hold uncomfortable positions when they are in pain or worried; perhaps having to go through narrow tubes which are sometimes incredibly loud; needing injections which makes them feel weird or mean they are radioactive and have to keep distance from other people at a time when they could really do with support. With research, we can aim to improve these experiences; reduce scan times or radiation exposures, wider bore scanners or open scanners, even finding new imaging or testing which removes the need for the ionising radiation all together!

Patients are at the heart of the NHS, and diagnostic imaging is often an area in which a good patient experience can be harder to provide (that is not to say that we don’t try!). With research, we have the potential to make those improvements to service, to provide for our patients in the way which we want to and the way that they deserve.

How can I get involved?

Often, radiography research is overseen by radiologists, doctors and orthopaedic surgeons, but there is no reason why radiographers shouldn’t also get involved. As we are the ones who use the equipment on a daily basis, consent and care for the patients during imaging, and come into contact with the faults and issues. Our profession contains a wealth of knowledge which can be used to improve all aspects of radiography.

You don’t need any academic qualifications to get started with research activities. In fact, many radiographers image research patients without being privy to the research aims. In a busy department, such patients are treated the same as any other in most regards. If you do image a research patient, perhaps look into the trial itself. As well as being interesting additional information, it can be used as material for CPD in the form of a case study or reflective piece. You may also discover potential ways to improve the patient experience within your department and help to enact future change.

Look into what your hospital requires for research involvement. The Good Clinical Practice (GCP) qualification, which is usually necessary, can be found for free as an online e-learning module. The NIHR website provides a lot of helpful information for getting started. NIHR also provides help for those who are wanting to gain further academic qualifications, such as through grant applications for fellowship awards. These are highly competitive but allow better access for NHS employees to undertake Masters or Doctorate level qualifications. The NIHR also run conferences for those who are new to research but interested in how they can take part.

You could also look at taking part in your department’s audits. Audits are a great way to check in on the health of your department, what you are doing well, and what you can improve on. Audit skills can also overlap with those necessary for research work, as well as provide possible avenues for research within your department.

How do I get a research job?

The roles of radiographers in research are expanding. Some hospitals offer clinical research radiographer positions, which give additional responsibility to train for and undertake specialised research imaging, often alongside a multi-disciplinary team. Other trusts may offer training for those wanting to aid research trials.

For research-specific roles, take a look at NHS jobs. You will find posts for Research Radiographers or Research Clinical Practitioners/AHPs. When I applied for my Audit and Research radiographer post, I had no specific research skills however I was well versed in audits and had learned about the processes of research in my own time. Enthusiasm goes a long way when applying for research roles, we need radiographers who are driven and raring to get stuck in.

There is so much experience and knowledge that radiographers have to offer research, and there’s so much improvement and advancement to be received in turn. I strongly encourage any radiographers to give it a try. You never know, you may get hooked!

To submit your research to a BIR journal find out more here:

BJR https://www.editorialmanager.com/bjr/default2.aspx

BJR Case|reports https://www.editorialmanager.com/bjrcr/default2.aspx

BJR|Open            https://www.editorialmanager.com/bjro/default2.aspx

About Kim Mason

Kim Mason is a HCPC registered diagnostic radiographer, graduating from the University of Leeds with a 1st Class BSc Honours in Diagnostic Radiography in 2018. They have experiences in education both inside and outside of radiography, and have a passion for improving the radiography services in the UK.

Kim has multiple chronic conditions, and as such, they are an ambulatory wheelchair user. This has given them keen insight into the experiences of patients within the radiography department, having undergone imaging in most modalities as a patient. They have a vested interest in educating the public about radiography, and educating radiographers on improvements to patient care. 

Can We Upskill Radiographers through Artificial Intelligence? 

Shamie Kumar describes how AI fits into a radiology clinical workflow and her perspective on how a clinical radiographer could use this to learn from and enhance their skills.

AI in radiology and workflow

We all know that AI is already here, actively being implemented and used in many trusts in seeing its real world value supporting radiology departments to solve current challenges. 

Often this is focused on benefits to radiologist, clinicians, reporting radiographers, patients, and cost savings, but what about clinical non-reporting radiographers undertaking the X-ray or scans – can AI benefit them too?

Let’s think about how AI is implemented and where are the AI outputs displayed? 

If the AI findings are seen in PACS, how many radiographers actually log into PACS after taking a scan or X-ray? Good practice is seen to have PACS open to cross-check images that have been sent from the modality. Often this doesn’t happen for various reasons but maybe it should be a part of the radiographers’ routine practice, just like post-documentation is.

Can Radiographers Up-Skill?

Given the view it does happen, radiographers will have the opportunity to look at the AI outputs and potentially take away learnings on whether the AI found something that they didn’t see initially or whether there was a very subtle finding. We all know people learn through experience, exposure, and repetition, so if the AI is consistently picking up true findings, then the radiographer can learn from it too.

But what about when AI is incorrect – could it fool a radiographer, or will it empower them to research and understand the error in more detail?

As with many things in life, nothing is 100% and this includes AI in terms of false positive and false negatives. The radiographers have the opportunity to research erroneous findings in more detail to enhance their learning, but do they actually have time to undertake additional learning and steps to interpret AI? 

CPD, self-reflection, learning through clinical practice are all key aspects of maintaining your registration, and self-motivation is often key to furthering yourself and your career. The question remains: are radiographers engaged and self-motivated to be part of the AI revolution and use it to their professional benefit with potential learnings at their fingertips? 

There have been a few recent publications that share insight on how AI is perceived by radiographers, what is their understanding, training and educational needs.

Many Universities like City University London and AI companies like Qure.ai are taking the initial steps in understanding this better and taking active efforts in filling the knowledge gap, training and understanding of AI in radiology.

Radiographers who are key part of any radiology pathway, are yet to see the real-world evidence on whether AI can upskill radiographers, but there is no doubt this will unfold with time.

About Shamie Kumar

Shamie Kumar

Shamie Kumar is a practicing HCPC Diagnostic Radiographer; graduated from City University London with a BSc Honors in Diagnostic Radiography in 2009 and is a part of Society of Radiographers with over 12 years of clinical knowledge and skills within all aspects of radiography. She studied further in leadership, management, and counselling with a keen interest in artificial intelligence in radiology.

References

Akudjedu, T. K. K. N. M., 2022. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. Journal of Medical Imaging and Radiation Sciences.

Coakley, Y. M. E. C. M. M., 2022. Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging. Radiography International Journal of Diagnostic Imaging and Radiation Therapy, 28(4), pp. P943-948.

Malamateniou, K. P. W. H., 2021. Artificial intelligence in radiography: Where are we now and what does the future hold?. Radiography International Journal of Diagnostic Imaging and Radiation Therapy, 27(1), pp. 58-62.

Kumar, D., 2022. CoR endorsed CPD Super User Training by Qure.ai. [Online]
Available at: https://www.qure.ai/gain-cor-endorsed-super-user-training/
[Accessed 23rd January 2023].

Is Artificial Intelligence a glorified red dot system?

Shamie Kumar

We are all familiar with the concept of artificial intelligence in radiology and its application that is expanding rapidly. But how will AI in the workplace affect the radiographer and how does it differ from the red dot system radiographers are so familiar with?

Shamie Kumar describes her perspective on how radiography has evolved over time, the impact radiographers can have in detecting abnormal X-rays and reflects how she views fast approaching AI in advancing current skills.

The Red Dot System

Often one of the first courses a newly qualified radiographer attends is the red dot course. This course demonstrates pathologies and abnormalities often seen in X-rays some obvious, others not, giving radiographers the confidence to alert the referring clinician and/or radiologist that there is something abnormal they have seen.

The red dot system is a human alert system, often two pairs of eyes are better than one and assist with near misses. How this is done in practice can vary between hospitals, in the era of films the radiographer would place a red dot sticker on the film itself before returning it to clinician or radiologist. In the world of digital imaging this is often done during “post documentation” a term used once the X-ray is finished, the radiographer will complete the rest of the patient documentation to suggest the X-ray is complete, ready to be viewed and reported. As part of this process the radiographer can change the status of the patient to urgent along with a note for what has been observed. From this the radiologist knows the radiographer has seen something urgent on the image and the patient appears at the top of their worklist for reporting and, so the radiologist can view the radiographer’s notes.

The Role of AI in Radiology

Artificial Intelligence (AI) is moving at a pace within healthcare and fast approaching radiology departments, with algorithms showing significant image recognition in detecting, characterisation and monitoring of various diseases within radiology. AI excels in automatically recognising complex patterns in imaging data providing quantitative assessments of radiological characteristics. With the numbers for diagnostic imaging requests forever increasing, many AI companies are focusing on how to ease this burden and supporting healthcare professionals.

AI triage is done by the algorithm based on abnormal and normal findings. This is used to create an alert for the referring clinician/radiologist. It can be customised to the radiologist, for example colour-coded flags, red for abnormal, green for normal, patients with a red flag would appear at the top of the radiologist worklist. For the referring clinicians who don’t have access to the reporting worklist, the triage would be viewed on the image itself with an additional text note suggesting abnormal or normal.

Image courtesy of qure.ai

What does AI do that a radiographer doesn’t already? AI is structured in a way that it gives the findings; for example, a pre-populated report with its findings or an impression summary and its consistent without reader variability. So, the question now becomes what can AI do beyond the red dot system. Here, the explanation is straightforward. Often a radiographer wouldn’t go to the extent of trying to name what they have seen, especially in more complex X-rays like the chest where there are multiple structures and pathologies. For example, a radiographer would mention right lower lobe and may not go beyond this, often due to confidence and level of experience.

AI can fill this gap; it can empower radiographers and other healthcare professionals with its classification of pathologies identifying exactly what has been identified on the image, based on research and training of billions of data sets with high accuracy.

The radiographers may have the upper hand with reading the clinical indication on the request form and seeing the patient physically, which undoubtably is of significant value. However, the red dot system has many variables specific to that individual radiographer’s skills and understanding. It is also limited to giving details of what they have noted to just the radiologist. What about the referring clinician who doesn’t have access to the radiology information system (RIS) where the alert and notes are? Do some radiographers add a text note on the X-ray itself?

Summary

Yes, AI is a technological advancement of the red dot system and will continue to evolve. It is structured in how it gives the findings and does this consistently with confidence adding value to early intervention, accurate patient diagnosis, contributing to reducing misdiagnosis and near misses. AI is empowering radiographers, radiologists, referring clinicians and junior doctors by enhancing and leveraging their current knowledge to a level where there are consistent alerts and classified findings that can even be learned from. This doesn’t replace the red dot system but indeed enhances it.

The unique value a radiographer adds to the patient care, experience and physical interaction can easily be supplemented with AI, allowing them to alert with confidence and manage patients, focusing the clinician time more effectively.

About Shamie Kumar

Shamie Kumar is a practicing HCPC Diagnostic Radiographer; graduating from City University London, BSc Honors in Diagnostic Radiography in 2009 and part of Society of Radiographers with over 10 years of clinical knowledge and skills within all aspects of radiography. She studied further in leadership, management and counselling with a keen interest in artificial intelligence in radiology.

Unlock the advantage of time across the breast health continuum of care

Tim Simpson

Time is everything when it comes to breast cancer care. If we can embrace smarter technology, this will help to provide better workflow efficiency and clinical confidence across the patient pathway, unlocking that much needed time to care for breast cancer patients. Tim Simpson General manager, UK and Ireland at Hologic explores how we can achieve this across the breast health continuum of care.

More accurate and efficient detection is instrumental for better patient outcomes. 3D Mammography™ systems have become smarter, bringing breast cancer diagnosis to a new level, improving cancer detection accuracy, optimising workflow, and supporting personalised patient care[1]. The integrated AI powered software solutions employ machine-learning and deep learning algorithms developed and trained on a large number of tomosynthesis (3D Mammography™) images to aid cancer detection, assess breast density, and accelerate diagnosis.

What’s more, using 3D Mammography™ can result in up to 40% fewer recalls[2], [3], helping to reduce the physical and emotional burden on patients and giving back valuable time to health care professionals.

To mitigate the time challenges typically faced when reporting tomosynthesis images, advanced imaging technology can reconstruct high-resolution tomosynthesis slices which results in a reduction in radiologist reading time.

More efficient detection can also be achieved when performing a contrast mammography examination. It’s possible to combine the power of Contrast Enhanced Mammography (CEM) with 2D and tomosynthesis images all in one compression to provide anatomical and functional imaging in a singular exam. The use of comprehensive imaging using co-registered functional and morphological information can reduce reading time to seven – ten minutes versus thirty to sixty minutes for a standard breast MRI[4],[5].

Almost 43% of women over 40 years old have dense breast tissue that can obscure lesions on traditional 2D mammograms, making cancers harder to detect and recalls more likely[6]. Women with very dense breasts have a four to five times greater risk of developing breast cancer in comparison to women with less dense breasts[7].

This is where new AI–powered technologies have the potential to help identify women who are particularly at high risk of breast cancer, specifically those women with extremely dense breasts.

Assessing women using automated breast density analysis software is a simple way to ensure that those most at risk of developing breast cancer are prioritised for screening, on potentially a more regular basis, whilst the screening interval for those women at lower risk could be extended, creating a more efficient and personalised breast screening program in the longer term.

Diagnostic innovation is on a trajectory that we cannot ignore. It is evident that AI is sure to revolutionise healthcare. There will be multiple benefits associated with the adoption of AI technology in breast imaging for patients and clinicians alike; for example, enhanced clinical confidence, improved workflow efficiencies, accelerated disease detection and increased accuracy of breast cancer diagnosis. Hologic is proud to be leading the way with its AI solutions for our customers and partners, helping to save time and lives across the breast health continuum of care.

1 Philpotts L, Kalra V, Crenshaw J, Butler R ‐ Radiological Society of North America 2013, SSK01‐09

[2] Friedewald SM, Rafferty EA, Rose SL, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014 Jun 25;311(24):2499-507.

[3] Olivia DiPrete, Ana P. Lourenco , Grayson L. Baird, Martha B. Nov 2017. Mainiero. Screening Digital Mammography Recall Rate: Does It Change with Digital Breast Tomosynthesis Experience?. Radiology: Volume 286: Number 3—March 2018

[4] Cancer.org. 2022. What Is a Breast MRI? | Breast Cancer Screening. [online] Available at: <https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection/breast-mri-scans.html&gt; [Accessed 28 April 2022].

[5] Julie Sogani,a Victoria L. Mango,a Delia Keating,a Janice S. Sung,a and Maxine S. Jochelson. Contrast-Enhanced Mammography: Past, Present, and Future. Clin Imaging. 2021 Jan; 69: 269–279.

[6] Sprague BL, Gangnon RE, Burt V, et al. Prevalence of mammographically dense breasts in the United States. J Natl Cancer Inst. 106(10), 2014.

[7] Ingrid Schreer. Dense Breast Tissue as an Important Risk Factor for Breast Cancer and Implications for Early Detection. Breast Care (Basel). 2009 May; 4(2): 89–92.

ADS-03644-EUR-EN Rev 002

See the latest BIR special publication on Breast Imaging and AI

Adapting to a new way of treating

Dr Ben George

Over the last 18 months, GenesisCare has treated more than 170 patients on the UK’s first ViewRay MRIdian MR-linac and adopted SMART planning as a new way of working. Here, Ben George explains why this latest hypofractionated technique has proven to be one of the success stories of the COVID-19 era.

Stereotactic ablative radiotherapy (SABR) is growing in importance in the curative cancer pathway. Increasingly, it offers patients the opportunity to enjoy relatively long periods of disease control where previously they would have been considered for palliative treatments. During COVID-19, the scales have tipped even further in favour of hypofractionated techniques because protocols have been revised to limit the risk of patient infection. More recently, attention has turned to stereotactic ablative MR-guided adaptive radiotherapy (SMART) – the most exciting development in radiotherapy for years, with the potential to treat previously inaccessible targets.

GenesisCare has been the first in the UK to adopt SMART, installing the first ViewRay MRIdian MR-linac just over a year ago. Since then, we have treated over 170 patients, some of which are the most challenging in the world from a radiotherapy perspective, such as pancreatic, central lung and now renal cell carcinomas. MRIdian sits within our SABR offering, which is run by a specialist team of oncologists, physicists, dosimetrists, and radiographers. Over an intensive 18 months, we have adopted a completely new way of working and overcome the challenges of a pandemic to treat patients not just from across the UK, but also from around the world.

SMART explained

The MRIdian MR-linac combines a 0.35 T split superconducting magnet with a 6 MV linear accelerator. This gives it unique advantages over conventional external beam radiotherapy linear accelerators, which rely on kV cone-beam CT (CBCT) imaging, and enables an entirely new approach to treatment.

First, using MRI instead of CBCT provides superior soft-tissue visualisation. This increased imaging capability allows the treatment to be adapted at each fraction based on the daily position of the target and nearby organs at risk (OARs). This is in marked contrast to external beam treatment with CBCT, where anatomy captured in the CBCT is simply rigidly matched against a planning CT. This rigid registration is then used to calculate the movements required to shift the patient into the correct position for treatment.

Second, the MRIdian takes images continuously throughout the treatment period to not only monitor the patient position, but also turn the treatment beam on and off. This is carried out as the patient’s anatomy moves through the breathing cycle.

This combination of enhanced visualisation and real-time imaging adds a layer of certainty in the delivery of treatment.

  • The MRIdian on-table adaptive planning system generates a new, optimised treatment plan for each fraction. This accounts for these day-to-day anatomical variations when the patient is in the treatment position.
  • Treatment delivery is then automatically gated so that the dose is only delivered when the target is in the optimal position. The machine is able to monitor every intrafraction motion caused by breathing or organ-filling.

As a result of these factors, we can design plans which deliver a higher dose, more precisely than with conventional SABR. There is no need for invasive fiducial marker insertion and any uncertainty is removed. Moreover, we can reduce planning target volumes, remove internal target volumes, and minimise the amount of tissue irradiated.

SMART has led to a paradigm shift in how some cancers are treated. In particular, it can benefit cancers in areas where there is significant inter- or intrafraction motion of either the target or OARs. Across the global community, MR-linac centres are now treating novel indications, such as renal, central lung and hepatobiliary tumours, and achieving clinical outcomes not previously thought possible. It is not simply a case of improving on an existing treatment – for some tumour types, SMART is facilitating new referral patterns for patients who may not typically be eligible for radiotherapy.

Pancreatic cancer – a new way of treating

Pancreatic cancer is one such example and of all the tumour sites we are now treating at GenesisCare, this is undoubtedly the one that is breaking most ground, offering new hope for both clinicians and patients.

For decades, surgical resection and adjuvant chemotherapy and radiotherapy have been the cornerstones of primary and secondary hepatobiliary tumours and pancreatic cancer treatment. However, options are limited for many patients. Less than 20% are resectable at diagnosis and not all patients are fit enough for an operation or effective chemotherapy regimens. There is, however, emerging evidence of a dose-response relationship, proving that escalated radiation doses are associated with improved local control as well as overall survival in borderline resectable (BRPC) or locally advanced pancreatic cancer (LAPC). Conventional radiotherapy delivers a comparatively homogenous radiation dose to the target volume. In contrast, SABR treatments combine advanced image guidance systems, accurate dose delivery and hypofractionated regimes. This is to facilitate a deliberate heterogeneous dose distribution across the target. This means the radiation tolerances of surrounding OARs are respected, while the tumour receives a higher, ablative radiation dose. A number of SABR studies have yielded good results in the treatment of large hepatobiliary tumours, with 1-year local control exceeding 90% and acceptable toxicity. Furthermore, delivering these hypofractionated ablative doses of radiation over a shorter treatment schedule has the potential to reduce the burden of treatment on patients.

However, with conventional SABR this therapeutic approach is often limited by concerns regarding organ motion and the possibility of developing small bowel radiation toxicity. As a result, many patients are only being treated with systemic agents. This is a prime example of where the elements of SMART on an MR-linac can facilitate an effective radiation dose escalation, while still respecting the radiation tolerance of normal tissues and surrounding OARs. In fact, using an MR-linac, it has been possible to successfully increase the prescribed dose in patients with primary pancreatic cancer. The previous standard dose was 33 Gy in five fractions, but SMART enables us to escalate the prescribed up to 40 Gy or even 50 Gy in five fractions. At the time of writing, 30 patients have been treated on the MR-linac for pancreatic tumours at GenesisCare.

Compassionate Access

The significance of MR-linac as an innovation in cancer treatment can’t be understated and, although at GenesisCare we are offering it in a private setting, we are committed to sharing the benefits of this technology with the wider medical community. Patients with localised pancreatic cancer have variable access to precision radiotherapy in the UK. The n-SARS-CoV-2 pandemic has further disadvantaged this patient group by reducing the availability and safety of surgery and chemotherapy. Considering this, since 2020 GenesisCare in association with GenesisCare Foundation, UK charity, Pancreatic Cancer Research Fund, ViewRay and University of Oxford have been treating NHS patients with localised pancreatic cancer with SMART at no cost. The programme, which is run through a partnership with the University of Oxford, is generating preliminary clinical and patient-reported outcome data on a UK cohort. This will inform the design of subsequent randomised clinical trials and help to embed SMART in UK oncology practice.

A new way of working

With any new technology, there comes a learning curve. MR-linac represents a significant change in working practices. It demands a style of inter-disciplinary working which challenges the norms.

In a standard radiotherapy workflow, a patient will receive a treatment planning CT one to two weeks before the start of treatment. During this time, several steps are carried out by a team of dosimetrists, physicists, doctors and radiographers to produce a treatment plan ready for the patient’s first fraction. These steps include contouring the treatment target and OARs and optimising the machine parameters to deliver the prescribed dose to the target while sparing critical structures. This is followed by reviewing the dose distribution, checking the planning process to ensure no errors have occurred and performing an independent dose calculation.

As part of the on-table adaptive workflow, the time taken for this process must be reduced from days to minutes. In order to achieve this, close inter-disciplinary working between the team is required. The need to undertake a number of complex tasks during each adaptive treatment also increases the time for each fraction to around one hour.

The MRIdian workflow involves a Clinical Oncologist on-site during treatment to oversee the daily adaption. To maintain a treatment schedule at GenesisCare, this has meant that clinicians had to be trained to contour all areas of anatomy, often working outside their main area of specialism. Equally challenging was the need to acquire skills in MRI interpretation, which for some specialities is not routinely used as a diagnostic modality. These were all skills that needed to be honed and validated before any patients could be treated on the MR-linac. In our case, we spent many hours learning with colleagues in MR-linac centres of excellence around the world. Twelve months later, we are experts in this field and have treated over 170 patients.

A body of evidence

There is a growing body of data as the global MR-linac community treats ever more and complex cases. We brought this international best practice to GenesisCare and have treated complex and challenging cases, including central lung, pancreas and reirradiation within our first year. We have many case studies available on our website genesiscare.com/mridian/case-studies. We already knew that the technology could deliver, but it was the confidence in our processes and the ability of our team to implement an adaptive workflow in a time-pressured environment, with a patient on the treatment table, which allowed us to embrace the opportunity that MR-linac presents in radiotherapy.

GenesisCare will install the second MR-linac in the UK in 2021. Through our MagNET programme, we are joining with NHS organisations to support education in the use of MR-guided radiotherapy. Enquiries to: James.Good@genesiscare.co.uk

Dr Ben George, Lead Physicist – MR Linac, GenesisCare

Ben is Lead Physicist – MR Linac at GenesisCare UK. He works as part of a multi-disciplinary team which has established a successful and world-leading SABR service delivering complex MR-guided adapted treatments. He has a PhD in Physics with a strong background in computer science, research and clinical computing. He has over ten years of experience as a Clinical Scientist specialising in radiotherapy in both the NHS and the private sector, and as a research scientist for the University of Oxford.

A podcast radiating positivity

Angela Young explains how the process of making a podcast helped not only others with a diagnosed brain tumour but gave comfort and support to herself as she embarked on a course of radiotherapy.

A brain tumour diagnosis, like all major events, can set in place a chain of emotions, among them anger, fear and denial. It can also make you adjust your priorities in life. I went through all this in 2015 when I discovered I had a Grade 1 benign posterior fossa meningioma. A resection at Addenbrooke’s Hospital in Cambridge was very successful, leaving only a 3mm residuum.

I had been having regular follow up scans, and in 2019, it was thought the growth was significant enough to consider radiotherapy. After the initial shock, I realised that, if successful, it would prevent the cells from growing again and remove the need for annual scans with the associated “scanxiety”. My decision to go ahead now rather than wait for symptoms to appear was influenced by the consultant radiologist Dr Sarah Jefferies who said the benefit of doing so now was that I was “young and fit”, a nice thing to hear at the age of 59.

As a journalist and podcast maker, I am used to getting to grips with a variety of subjects quickly in order to explain them to others. It dawned on me that if I could tell the story of my own treatment, it would give me a sense of control over a process in which one can easily feel helpless. It might also provide information and some light relief to other people going through something similar and their families. The radiotherapy process would be the same for people undergoing treatment for a variety of conditions, not just brain tumours, and so creating a podcast on this topic could reach and potentially help a large audience.

I am very optimistic by nature and I like to see the funny side of things. I believe that if you look closely, you can find humour in most situations. Consequently, I decided the title of the podcast should be “A Sense of Tumour”. I started recording everything that happened, whether by phone call (I had got all the kit I needed for doing this when lockdown started) or recording my own commentary during appointments and tests and arranging interviews, either face to face (with masks on) or via an audio recording platform.

People find podcasts in a variety of ways. One of those is to have a well-known personality or influencer or support group post about them. It helps if you can interview a celebrity or two who will do this. When I asked Victoria Derbyshire (via a mutual friend) if she would talk to me about documenting her very public battle against breast cancer, I had no idea she would later be taking part in the TV programme “I’m A Celebrity, Get Me Out Of Here!”. Victoria appeared in Episode 1 and set the interview bar quite high. Luckily, the Brain Tumour Charity had come on board by this stage and offered to put me in touch with TV presenter Nicki Chapman, who had had a matching meningioma to mine removed last year. She readily agreed to be interviewed and candidly shared the highs and lows she experienced when going through treatment herself. For the final episode, I thought I would chance my luck and ask to speak to Tony Iommi, lead guitarist and song writer with Black Sabbath. He had had radiotherapy a few years ago and embraced some alternative therapies which I wanted to hear about. To my delight, he was more than willing to talk.

The series was meant to inform as well as entertain so I spoke to the medical professionals whom I was meeting and also those at the cutting edge of research into treatment. I interviewed the Chair of Cancer Research UK, Sir Leszek Borysiewicz, about funding for brain tumours. I also had conversations with the “Distinguished Scientist” from Elekta, one of the companies which makes the linear accelerator machine (not a bad job title!) and to many people from the team at Addenbrooke’s, including a medical physicist and a research radiographer. I learned a lot and I hoped that sharing these conversations would also help listeners to understand some of the more complicated parts of the treatment and process more easily.

Bringing the podcast’s listeners on my journey was supposed to feel personal too. I recorded as much as I could at every stage, including the baseline neurological assessment. This is an IQ-style test carried out before the start of a course of radiotherapy to the brain so that if there is any concern about future cognitive function, there is a baseline against which to compare it. One part of the test included listing as many words as possible beginning with the letter F; you can imagine what came to mind! When that episode was released, listeners I came across would shout out words beginning with F to me.

All the way through the treatment I was thinking how I would represent things aurally, such as the MRI machine. These make a variety of loud noises but would wreck any recording device in the vicinity. When I managed to open my eyes under the thermoplastic mask which holds the head in place on the linear accelerator, part of the machine going over me looked like a spaceship. Friends and family had each contributed a song for my radiotherapy playlist; that day the song was Mr Blue Sky and it had got to the instrumental part, which made me think of a science fiction movie. I was working out how to recreate the impression for the podcast. Thinking about this during the session took my mind off what was going on.

By the time you read this, I will have finished the treatment and will be waiting for a scan to see how successful it has been. I am, of course, hoping for the best. I would also like to think that the podcast series has been useful to patients and their families, to radiotherapists, to manufacturers and anyone else involved in this fascinating process. I also hope that it inspires anyone looking for a positive and creative way of dealing with a diagnosis of any kind to take control of what they can, focus on something meaningful and use their good days to bring strength to others. After all, positivity radiates.

Listen to “A Sense of Tumour” here

About Angela Young

Angela Young founded Cambridge Podcasts in 2018 to help clients showcase their expertise and establish themselves as the go-to person in their field. She is a former BBC radio journalist who has worked as a reporter, producer, news reader and news editor. She has taught law and journalism at the BBC and media handling at the prestigious Institute for Management Development in Lausanne. She studied law at Cambridge as a mature student and has lived in the city for 28 years. www.cambridgepodcasts.co.uk info@cambridgepodcasts.co.uk

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The five-step guide to AI adoption in clinical practice

Jeroen van Duffelen proposes a five-step programme for adoption of artificial intelligence in clinical practice.

Adoption of medical imaging AI is about getting your hospital or screening programme ready to implement the right solution for a clinical need. Running into speed bumps along the way is common for early adopters. How do you define the needs, budget, and outcomes? Which boxes should you check when selecting vendors? How do you manage internal stakeholders? The adoption curve is steep. Luckily, you don’t have to climb it alone.

Drawing from our experience deploying AI in clinical practice and lung cancer screening, I’ve designed a five-step guide to streamlined adoption. If you’re looking to adopt artificial intelligence but don’t know where to start, these actionable tips and advice will see you through. For the video breakdown of the steps, watch this presentation from ECR 2020.

1. Consider

Where do you start working with AI? First, look away from all the solutions out there, and focus on your organisation. Bring together all the stakeholders into a project team that includes the sponsor, if applicable, IT and legal representatives. Involving them from the beginning will expedite the process.

Start with defining the challenge you are looking to solve, or the specific clinical question that is relevant to your workflow. Some hospitals are looking to experiment with the technology, while others aim to solve a particular issue. Over the past years, I have seen the latter getting more out of AI, which is why my advice is to start from a clinical challenge.

When considering this challenge, make sure to already determine your expected outcomes. When is the adoption a success? Are you aiming to have an AI solution in use? Should it apply to a certain patient population, or yield specific results like time or cost savings?

Also, although it may seem early, this is also the stage to organise a budget dedicated to the AI solution. The size of this budget should relate to the cost or time savings a solution is expected to bring. Both the amount secured and its source will impact the next steps. For example, it will guide you to look for PhD researchers versus seeking a vendor that offers a mature solution.

2. Evaluate

The AI in healthcare space is widely populated; a Google search or a look at the list of vendors at the RSNA can confirm that. To weigh the existing options for your scope, do your (desk) research using this high-level checklist for each solution:

How was the AI solution validated?

It is important that the claim that the AI solution has validated covers the use case you identified in the previous step. Take the time to understand if the manufacturer has done studies confirming this claim.

How does it integrate into the workflow?

Try to get a feel of the amount of effort needed to add an AI system into your workflow. A good practice is to start with an AI solution that is easy to integrate with the current workflow and IT infrastructure. Workflow integration is of utmost importance for the radiologist; in this article, we explained why that is and how it works.

What regulations does the solution fulfill for use in clinical practice?

Commercialising medical devices requires a CE Mark in the EU and an FDA clearance in the US. Note that local regulations may apply to different countries. Again, pay attention to which claim is covered by the acquired certification.

3. Choose

By this stage, you should have narrowed your search down to a few vendors. This is the moment to go in-depth into the workflow and test if a specific solution is a good fit from both a clinical and a technical standpoint. A well-integrated AI system should not create hurdles for physicians, such as requiring them to leave their workstation to upload studies. It should further blend within the existing IT infrastructure.

There are two checks that are vital to make the right choice:

Validate the accuracy

Legitimate vendors would have done a study and can provide a clinical background for accuracy. To know if the solution is good at performing the defined task for your organisation, ask questions about the datasets used to develop and test the AI solution.

There are three datasets required to build an AI model: a training dataset, a validation dataset, and a test dataset.

The test dataset is the most relevant to look at because it is what the accuracy is based on. The performance on this dataset should be applicable to your hospital, with its specific protocols, type or number of scanners, and patients. To achieve this, the test set must cover the patient population your organisation serves (e.g. types of patients, comorbidities distribution, etc.). Thus, inquire about the specifics of the test set and the performance of the AI model on this dataset.

Secondly, you may want to know what the size of the training dataset is and how it was labelled. Both quantity and quality are important to train an accurate AI model. Labeling the data should be done by experienced radiologists, preferably with multiple readers per study.

Check the regulatory compliance

In Europe, medical device classification is divided up between risk Class I, Class IIa/b, or Class III. If looking for a solution for clinical practice, be wary of Class I medical devices. The new Medical Device Regulation, which will come into force in May 2021, will require many AI products currently classified as Class I devices to update their classification. For instance, software that supports diagnostic decisions should fall under Class II at a minimum. For more guidance on the new regulation, read our recent expert piece.

Apart from the regulatory approval, check if the vendor also has a quality management certification (e.g. ISO 13485). Reviewing the data processing policy and the cybersecurity measures in place will further help you understand if the AI company is going the extra mile in regard to safety.

A bonus tip for the choosing stage: do a reference check. Ask other organizations how they are working with the AI solution you have chosen. You may get the insights you need to make the final decision.

4. Approve

Approving the chosen solution internally requires the involvement of and coordination between IT and PACS administrators, procurement officers, physicians, often also privacy departments and legal officers. If you have a project team in place since the first step, you should be well on track.

To move forward and avoid delays, assign an internal AI champion responsible for driving the project. This may be an executive sponsor, a budget holder, or a department manager. One of my learnings from past deployments is that the risk of failure is high without a person fulfilling this role. What I have further learned as vendors is the importance of empowering the AI champion, by providing the necessary information and documentation in a timely manner.

Furthermore, make sure end users are trained to use the new medical device. If they don’t benefit from it, the impact of the AI solution will be limited. Additionally, setting up a feedback mechanism with the AI vendor from the get-go will help improve the AI product.

5. Deploy (& evaluate)

All the paperwork is signed – well done! To make the deployment work, create a clear project plan, including actions, timelines, and owners. Depending on the type of deployment – on-premise or cloud-based – different actions will be needed. As outcomes, set the deployment and acceptance dates, make agreements on the service levels, fixes, and upgrades, and discuss post-market surveillance.

The initial or trial phase of using the AI solution should show if it answers the problem you were trying to solve. It is a good moment to revisit step one and start evaluating the results to decide if you will continue using the solution.

A common question I get at this stage is: “Do I need to do a full clinical study?” The answer fully depends on the purpose of using the product. It is necessary for research, but not for other use cases. What matters is validating that the AI solution is adding value to your clinicians and their patients.

Make it better

AI adoption does not end with deployment. Service and maintenance are essential, and their quality often a differentiating factor between AI vendors. The implementation process usually acts as a good test for the AI companies fulfilling their promises and being prompt when handling requests.

Beyond these five steps, you and your organisation play a role in improving the chosen AI solution through valuable feedback and feature suggestions. The collaboration between humans and software allows us to achieve much more than humans would on their own. If done right, it can be transformative for patients.

Are you ready to start the AI journey? Get in touch!

Jeroen van Duffelen, COO & Co-Founder

Jeroen van Duffelen is COO and co-founder of Aidence. Jeroen’s entrepreneurial spirit led him to teaching himself software engineering and starting his own company commercialising an online education platform. He then tried his hand in the US startup ecosystem where he joined a rapidly scaling cloud company. Jeroen returned to Amsterdam where he ran a high-tech incubator for academic research institutes, it is here Jeroen first got his taste for applying AI to healthcare.

Is artificial intelligence the key to effective and sustainable lung cancer screening?

Lizzie Barclay doctor

Dr Lizzie Barclay explores how artificial intelligence can influence lung cancer screening.

Radiology as the starting point

Imaging plays a fundamental role in lung cancer screening programmes. So, when it comes to improving technology to support the programmes, the radiology department is a good place to start.

The goal of screening is to pick up early cancers which can be treated and potentially cured, therefore improving patient outcomes (as outlined in the NHSE long term plan). Low dose CT has been shown to provide sufficient image quality for detection of early disease, whilst minimising radiation dose in asymptomatic individuals. Thoracic radiology expertise is required to determine which lung nodules may be malignant and therefore require invasive investigation, and which are likely benign and can be monitored with intermittent imaging. Appropriate follow-up recommendation helps avoid unnecessary invasive procedures, such as biopsies, and minimise patient anxiety, which are important measures of the efficacy of lung cancer screening programmes.

End to end lung cancer screening involves input from many healthcare professionals, and intelligent computer systems across specialities would benefit multidisciplinary teamwork. Thus, beyond image analysis, there are many opportunities for technology to add further support for effective and sustainable screening programmes. For instance, it could aid in the optimisation of image acquisition, access to imaging reports and relevant clinical details, tracking patient follow up, or in communication between patients and GPs.

Where AI-based image analysis makes a difference

Reading and reporting CT scans is time-consuming, and within a workforce which is already under strain, introducing a new CT-screening programme seems like a tall order. AI-driven solutions can support radiologists and contribute to successful lung cancer screening by bringing improvements in three areas:

  1. Performance

Computer intelligence can increase the performance and productivity of CT reporting, freeing up time for radiologists to spend on clinical decision making and complex cases. Specifically, AI software is well-suited for precise:

  • Detection of elusive lung nodules, and differentiation of subtle changes
  • Automatic volume measurements, to help determine the appropriate frequency of monitoring (e.g. stable vs growing nodule, according to the BTS guidelines).

What further distinguishes computers from humans is the absolute consistency in their high performance, without being impacted by common external stressors to which a radiologist would be exposed (e.g. time-pressure, workload and interruptions).

  1. (E)quality

Having a ‘second pair of eyes’ looking at the scan can increase the confidence of the radiologist in their own assessment. Additionally, making the AI-driven, accurate measurements available regardless of the level of expertise of the reporting radiologist could not only benefit quality assurance, but also equality within the radiology department. The use of AI would reduce the need for all scans to be reported by the most experienced thoracic radiologists with interest in early lung cancer detection, and instead facilitate spreading the workload across the workforce.

Another use case concerns quality assurance when outsourcing to teleradiology companies. AI-based image analysis can improve consistency of reporting, drive the recommended terminology use, and, essential for lung cancer screening, ensure access to relevant prior imaging for comparison and change assessment over time.

  1. Efficiency (via integration)

An intelligent computer system should not slow down reporting turnaround times, but improve efficiency, as well as quality, to ultimately minimize time to diagnosis (for example, the NHSE long term plan introduces a 28-days standard from referral to diagnosis or rule out).

Older CAD technology was often described as ‘clunky’ – requiring images to be uploaded to separate systems for analysis. Additional manual steps between image acquisition and the radiology report make the process time consuming, and often require radiology support staff to manage the workflow. It is important to consider allocative and technical efficiency which play important roles in the evaluation of screening programmes, and their impact on healthcare systems.

An AI-driven image analysis software which is fully-integrated in the radiologist’s pre-existing workflow can provide automatic results, without needing additional departmental resources. An additional benefit of fully-integrated AI solutions is that their use is not restricted by time or place, therefore supporting flexible and remote working. In the context of the COVID-19 pandemic, it’s been encouraging to see the increase in remote reporting, whilst maintaining a functioning department, in many hospital trusts. Going forward, it will be interesting to see whether radiologists will have the option to continue to work remotely where possible.

Valuing input from healthcare professionals

New lung cancer screening programmes will be monitored regularly to evaluate their effectiveness and determine areas for review. Commitment from all parties to work together will facilitate optimisation of the pathway to achieve better patient outcomes and positive impacts on healthcare systems.

In our experience, close collaboration between medtech and healthcare professionals is important for learning lessons along the way. Understanding radiologists’ needs helps tech teams develop a clinically valuable tool.

For example, Aidence’s interactive lung nodule reporting tool, Veye Reporting, was designed based on the needs of radiologists involved in reporting lung screening scans. From our conversations with them, we understood that following the detailed and complex reporting protocols in lung cancer screening programmes make for labour-intensive, repetitive tasks.

Veye reporting

To help them produce reports that follow the standardised NHSE proforma and facilitate audit for quality assurance, we added Veye Reporting as a feature to Veye Chest, focusing on making it easy-to-use and efficient. With this tool, the radiologists further have control over which nodules to include in the report, different sharing options, and the choice to add incidental findings.

What’s next?

Cancer services have been impacted by the COVID-19 health emergency. In the UK, screening has been paused and planning to (re-) start at the end of 2020 or beginning of 2021. Talks of introducing screening are ongoing in various European countries, as are concerns of catching up with the backlog of screening scans.

The British Society of Thoracic Imaging and the Royal College of Radiologists released these considerations for optimum lung cancer screening roll-out over the next five years. Their statement below is a reminder of why it is worth overcoming challenges and leveraging technology to make screening programmes a success:

BSTI_RCR statement

Dr Lizzie Barclay, Medical Director

Dr Lizzie Barclay’s areas of interest are thoracic radiology and medicine, innovation, and improving patient outcomes and healthcare professionals’ wellbeing.

Lizzie is originally from Manchester, UK. After graduating from the University of Leeds Medical School (MBChB), and Barts and the London School of Medicine (BSc sports & exercise medicine), Lizzie spent four years working as a doctor in Manchester and Liverpool NHS Trusts, including two years in Clinical Radiology. She has presented her work on lung cancer imaging at national/international conferences, and recently contributed to Lung Cancer Europe’s “Early Diagnosis and Screening” event at the EU Parliament in Brussels.

Homepage

You may be interested in the BIR Lung Cancer Imaging: Update for the not-so-new normalon 11 September 2020. This will be available for members in the BIR online learning libraryafter the live virtual event.

 

“The Radiology Reset Button – overcoming the normalcy bias”

Fodi KyriakosFodi Kyriakos explores how the COVID-19 pandemic could be the catalyst for change in radiology and encourages our community to grasp the opportunity to “seize the moment”  and plan for recovery.

At the beginning of 2020, if someone had told radiology leaders that all NHS outstanding reporting backlogs would be reduced to virtually zero by May, I’m sure they would have looked at you in disbelief and asked what sorcery had been involved, but this situation is exactly where we find ourselves today.

 

Normalcy Bias – Noun [edit]

normalcy bias (plural normalcy biases)

The phenomenon of disbelieving one’s situation when faced with grave and imminent danger and/or catastrophe. As in over focusing on the actual phenomenon instead of taking evasive action, a state of paralysis.

Historical challenges

In the past, it has often taken lots of effort to either invoke or accept change of any kind in radiology and for those managing services, there’s also been a certain amount of risk associated with putting your head above the parapet or being a trailblazer. It has been sometimes easier to follow the well-trodden path rather than to create a new one. Workloads and budgetary constraints have also been a disabler, restricting decision making to the ‘here and now’. This has resulted in failing, or in most cases, not being able to foresee or plan for events that have never happened before, such as an event like a pandemic crisis. Psychology refers to this state of being as normalcy bias. For those who are not familiar with the term, you will certainly be aware of its connotations and radiology now finds itself at this cross-roads.

Ever since the introduction of digital radiography and PACS, NHS radiology reporting backlogs have been a contentious issue among experts, and a recurring feature in the mainstream media! Often being highlighted (and with some justification) in relation to areas such as missed cancer diagnosis, where even the slightest of delays can have a significant bearing on the overall outcome.

Serious backlogs

The extent to which backlogs were a serious issue in the UK was further exacerbated by various Care Quality Commission (CQC)  inspections, which raised concerns regarding reporting backlogs that resulted in delayed or missed diagnosis of conditions that may have otherwise been picked up.

By the end of February 2020, the situation of backlogs was as much an issue as at any time before. Insufficient reporting capacity had led to a build-up of outstanding reports, which in turn meant that outsourcing was at its highest ever levels and growing pressures to meet new deadlines, such as the cancer pathway targets, were increasingly exposing the lack of options available to resolve the problem.

So, you would have been excused if you thought that a crisis such as the COVID-19 pandemic would simply exacerbate the reporting challenges facing radiology. However, this has not been the case. Instead, we have witnessed radiology’s own “clear the decks” exercise, where in fact the complete opposite situation has occurred, resulting in backlogs across the UK being virtually eliminated. Who would have thought that the worst crisis to hit the country (and the world) in 75 years would be a catalyst for NHS radiology departments to press the reset button?

Reset image

Of course, we recognise the superficial nature of this situation. During the pandemic, practically all routine referral activity came to a grinding halt, which allowed radiology to concentrate on COVID-19 and Emergency Department (ED) patients. Chest X-rays and CTs were identified as two of the key diagnostic tools for the virus, but the volumes were manageable. Accident and Emergency footfall was reduced to almost 50% of its usual figures, so reporters were practically able to deliver a ‘Hot-Reporting’ examination for every patient requiring imaging. Something which ED and Intensive care unit (ICU) consultants have grown quickly accustomed to.

During this time, radiology was also still required to work to critical staffing levels, so radiographers and radiologists were covering 24/7 rotas, but due to the lack of activity outside of portable X-ray scanning in ICU, many staff were not being utilised. So, while this enabled the catch up in radiology reporting to take place, what we witnessed was the ‘ying and yang’ of radiology. On the one hand, integral to the continuity of a patient’s pathway and critical to defining an outcome – AND on the other hand, completely dependent on throughput from referrers to maintain activity levels.

Seizing the moment!

So what happens next? Well, in a world where we can guarantee almost nothing, in this situation, we can guarantee that radiology will remain the centre point for the recovery phase of the pandemic, but with the added challenge of complying to ‘social distancing’ and ‘equipment cleaning’ guidelines, how do we manage the continuation of treating COVID-19 patients, while reintroducing ‘business as usual’ and ‘deferred’ patients whose treatment has been delayed?

The “Reset Button” has enabled something else to happen. For the first time, there is now some headspace to plan for the recovery phase and for the next phase at least, there is now funding available to support the recovery. So how do we avoid going back to where we were before the pandemic? How do we seize the moment?

Time to make the changes!

Albert Einstein once famously said: “We can’t solve problems by using the same kind of thinking we used when we created them.” This quote has never been more poignant in the present day and while the pressure to manage change will be at its highest, this is the right time to make these changes happen! With the benefit of ‘The Reset Button’, if we can learn from the past and apply new ways of working moving forward, we can avoid falling into the trap of the normalcy bias and witness the radiology reset button offering a new, efficient and more streamlined radiology department moving forward.

Everything you wanted to know about radiology but were afraid to ask…

On Wednesday 17 June, a live event organised by InHealth, in partnership with The British Institute of Radiology and the Society of Radiographers is taking place, titled: “The Radiology reset button has been pressed”. The aim is to tackle these challenges and support radiology managers as they enter the recovery phase. It will bring together senior figures from radiology and within healthcare to offer insights, opinions and advice on how we can approach this coming period and use what positives we have experienced during the pandemic to create service improvements throughout radiology.

There will be opportunities for radiology managers, clinical leads, radiographers and radiologists to put their questions to the speakers in the panel discussions after their presentations.

REGISTER FOR THE RADIOLOGY RESET BUTTON HAS BEEN PRESSED HERE

(The event is free for all)
About Fodi Kyriakos

Mr Fodi Kyriakos is a former director of RIG Healthcare and founder of RIG Reporting,
the UK’s first provider of external radiographer reporting services. In 2016 he joined The InHealth Group following its acquisition of RIG Reporting and is now the Head of Reporting across the Group. His service specialises in delivering plain film reporting solutions and is the only provider to offer both on-site and telereporting services.
Fodi has over 22 years experience in workforce and staffing solutions and 17 years working exclusive within Imaging and Oncology. He is a member of the Institute of Healthcare Managers and a regular contributor of professional development events across radiology.

 

Bringing together Science, Faith and Cancer Care

Slide2

The Revd. Canon Dr. Mike Kirby, Chair of the BIR Oncology and Radiotherapy Special Interest Group, has a wealth of experience as a senior radiotherapy physicist, working on national guidance, developing clinical practice and teaching radiography students. As if this doesn’t keep him busy enough he has also taken on the role of Canon Scientist at Liverpool Cathedral where he is working to encourage dialogue and discussion about science and faith. Here he explains what the role involves.

I began work in the UK’s National Health Service more than 30 years ago, as a Radiotherapy Physicist at the Christie Hospital, Manchester UK.  Alongside my routine clinical work, my main research interest was in electronic portal imaging and portal dosimetry.  I then helped set up Rosemere Cancer Centre in Preston, UK from 1996 as deputy Head of Radiotherapy Physics and Consultant Clinical Scientist there.  During that time I contributed to and edited national guidance documents such as IPEM Reports 92, 93 and 94 and the multidisciplinary work, ‘On-target’.

My work moved back to the Christie in 2007 and as Head of Radiotherapy Physics and Consultant Clinical Scientist for the Satellite Centres, I helped to lead their development in Oldham and Salford as part of the Christie Network. My research and development work has primarily focused on electronic portal imaging, developing clinical practice and equipment development.

Mike Kirby4

More recently my focus has been on teaching and learning for radiotherapy education as a lecturer (Radiotherapy Physics), especially using VERT, for Radiotherapy programmes in the School of Health Sciences, Liverpool University; but always with a focus on the wider picture of radiotherapy development having served on both IPEM and BIR committees throughout my whole professional career.

 

Alongside my scientific work, I am a priest in the Church of England; having trained and studied at Westcott House and the Universities of Cambridge and Cumbria, I hold graduate and postgraduate degrees in Theology.

Mike Kirby

My ministry has mainly been in the Cathedrals of Blackburn, Chester, and Liverpool (Anglican) where I was Cathedral Chaplain.  I have recently (Feb 2020) become a Residentiary Canon of Liverpool Cathedral, with the title of Canon Scientist the primary aim of which is to encourage dialogue and discussion about science and faith.

 I am a member of the Society of Ordained Scientists and have given numerous talks on Science and Faith to schools, colleges, churches and other institutions.  These have included organising lecture series with world renowned speakers at Blackburn (2016) and Chester (2018) cathedrals; a third series was delivered at Liverpool Cathedral in May 2019, and a fourth series is planned for May 2020.

My role is to consider all sciences (physical, clinical, social) in ecumenical and multi-faith environments.  So I will look to work with initiatives already developing in other Christian traditions, other faiths and secular organisations to discuss current challenges, such as climate change, medical ethics, health initiatives and information for cancer, dementia and mental health issues etc..

Mike Kirby2.jpg

My work will be part of the clear faith objectives of the cathedral as a place of encounter for everyone, through events and initiatives within the cathedral, but also beyond.  This will include services focusing on health issues and pastoral challenges (such as bereavement and loss); events engaging with science, its wonders and challenges; fostering further relationships with local and wider communities on science and healthcare education, and with academic and scientific institutions too; encouraging scientific and ethical engagement with schools and colleges, as I have done so previously in both Chester and Blackburn dioceses.

I will be encouraging Christians and Christian leaders to understand science and engage with it more, alongside other national projects such as the recently announced ECLAS (Engaging Christian Leaders in an Age of Science) project of Durham and York universities and the Church of England.  As a self-supporting minister (one whose paid employment is outside of the church), I will also look to encourage and highlight the tireless work of many others who already do this within the diocese and the wider national church.

Within all of this, I have always seen my vocation as being one within God’s service, for all people, with my work for cancer patients being right at the heart of it.

006

If you have any questions for Mike, you can send him an email at sigs@bir.org.uk

Mike is the co-author of the international student textbook on On-treatment Verification Imaging: a Study Guide for IGRT, through CRC press/Taylor and Francis with Kerrie-Anne Calder. They are both contributors to the updated UK national guidance on IGRT due out in 2020.

Mike, with the support of the SIG, has helped to organise a range of events for radiographers, physicists, dosimetrists, radiologists and oncologists. See the full programme here