Cancer cases are on the rise: preparing now will pay off in the long-run

Cancer care teams have faced unprecedented pressure over 2020.

But out of these challenges have come important learnings about how we rethink cancer care for the future.  

Cancer Research UK anticipates that there will be 514,000 new cancer cases per year by 2035, an increase of more than 40%. These are significant numbers and we can not underestimate the pressure this will put on healthcare teams. Especially given that we are experiencing international shortages across all key cancer care professionals from radiation oncologists to specialist cancer nurses. 

Rising cases, combined with a shortage of skilled staff, could lead to a dangerous crisis point for cancer care. Healthcare organisations must plan for this now in order to deliver the best possible patient outcomes. This is where technology has an important role to play.

Saving time to save lives

The 2020 cancer care backlog forced hospitals to innovate and deliver more effective cancer treatment in shorter time frames. One of the key changes was the wider roll out of hypofractionation, as mandated by the NHS. Hypofractionation optimises radiation treatment by giving patients fewer but a more concentrated number of treatments. In turn, patients receive faster outcomes and reduce their risk of exposure to other diseases during hospital visits. 

This approach will have an important role to play as the NHS scales up to meet future demand for cancer treatment. However, more powerful radiation treatments like hypofractionation require greater precision. This is where technology becomes instrumental. With this in mind, the NHS is also looking into Artificial Intelligence (AI) technology solutions to enhance the precision and speed of radiation therapy.

Putting AI in Perspective

AI is potentially a powerful way to automate elements of the radiotherapy treatment planning process. As an example, AI based Autocontouring is already drastically reducing the time-consuming and skill-intensive task of outlining organs at risk in the images from CT scans.

Discussing the impact of this technology, Angela Rubio, former Chief Medical Dosimetrist at the University of New Mexico Cancer Centre stated that: “Prior to using AI, contouring for a head and neck cancer patient would take about two hours to complete. With autocontouring it takes about 30 minutes. That is a 75 per cent time-saving for each head and neck patient. Essentially the technology is saving us seven hours a week; almost a full working day.”

The widespread use of AI autocontouring has the potential to deliver similar efficiencies across NHS cancer clinics.  This would enable time-pressured healthcare teams to treat more patients and subsequently help improve patient care now and in the future.

The potential of AI autocontouring to enhance cancer treatment has been recognised by the UK government. Secretary of State for Health Matt Hancock stated that: “the NHS is committed to fast-tracking pioneering AI technologies to the front line, freeing up clinicians time and saving lives”. As part of this commitment, the NHS recently announced a £140m AI in Health and Care Award programme. It was positive to note that one of the key awards focused on progressing AI-based medical imaging. 

Balancing the present and the future

While the government and healthcare practitioners recognise the value of AI autocontouring in cancer care, getting these solutions into the hands of frontline staff presents a major challenge. NHS staff are stretched to the limit and the resources are not available to facilitate the introduction of new technologies. 

Dr Rob Chuter, Principal Clinical Scientist at The Christie NHS Foundation Trust, states that additional support is needed to deliver AI technologies more widely. “Adopting and implementing new technologies into NHS infrastructure is a complex task requiring both investment and training. Clinicians just do not have the time for this at the moment, they are too busy to step off the conveyor belt. We need more resources for training and development to integrate AI technologies into cancer care, ” he states.

Some of the companies developing  AI autocontouring solutions are launching Accelerator Programmes to fast-track access to this technology. These programmes include hands-on support for the clinical staff and introductory periods to allow for the value of the technology to be assessed and funding sought. These programs are designed to support the NHS to move swiftly from implementation to impact.  

There is also a further incentive for the NHS to promote wide adoption of AI. The NHS is uniquely well-positioned to pioneer the data-driven optimisation of cancer care, powered by AI. By scaling the collection and aggregation of medical imaging data across UK hospitals, the NHS has the potential to utilise data as a resource like almost no other healthcare organisation in the world. 

Privacy of healthcare information and strict adherence to the rights of the patient are critical. However, if properly managed, this data insight can transform the delivery of cancer care and the selection of treatment pathways likely to deliver best outcomes for the patient. By working together in this way, NHS organisations and the private sector can lay the foundations for more effective cancer care now whilst also scaling up to meet future demand for treatment.

About the Author

Hugh Bettesworth has worked in the software industry for over twenty-four years, the last twenty in medical software and digital health. He joined Mirada Medical in 2001 and led a management buyout from Siemens in 2009 which re-established Mirada’s independent status. Since 2009, Mirada has experienced 10-fold growth and the company’s advanced software technologies have been deployed in around 2000 cancer treatment centres worldwide.

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