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Take a look at a selection of our recent media coverage:
3rd August 2023
The use of magnetic tentacles offer a novel therapeutic and targeted approach for minimally invasive lung cancer detection and treatment, according to researchers from the University of Leeds.
The researchers developed a tiny, magnetically operated robot that is capable of travelling deep into the lungs and able to detect and treat the first signs of cancer. Their approach makes use of a 2.4 mm diameter, ultra-soft, patient-specific magnetic catheter – or tentacle – which can be delivered from the end of a standard bronchoscope to reach the periphery of the lungs. In addition, the tentacles possesses a laser fibre designed to enable targeted photo-thermal therapy to cancer cells.
In their study, which was published in Nature Engineering Communications, the team initially developed a three dimensional model of the bronchial tree, down to the sub-segmental bronchi, using data generated from a CT scan of the full lung. Once the tentacle was in position, laser light was delivered through the embedded fibre to induce thermal ablation of the tumour.
Following this initial and successful virtual experiment, the researchers next used the magnetic tentacle robot on the lungs of a cadaver. They were able to successfully navigate in three branches of the left bronchi, compared to only two using a standard catheter, which corresponded to a mean improvement in navigation depth of 37%.
Commenting on the results, Professor Pietro Valdastri, the project‘s research supervisor, said: ‘This is a really exciting development. This new approach has the advantage of being specific to the anatomy, softer than the anatomy and fully shape-controllable via magnetics. These three main features have the potential to revolutionise navigation inside the body.‘
Lung cancer has the highest worldwide cancer mortality rate. In early-stage non-small cell lung cancer, which accounts for around 84% of lung cancer cases, surgical intervention is the standard of care. In addition to being able to navigate within the lungs during a biopsy, the magnetic tentacle robot could pave the way for far less invasive treatment, allowing clinicians to target only cancer cells while allowing healthy tissue and organs to continue normal function.
Dr Giovanni Pittiglio, who carried out the research as part of his PhD, added: ‘Our goal was, and is, to bring curative aid with minimal pain for the patient. Remote magnetic actuation enabled us to do this using ultra-soft tentacles which can reach deeper, while shaping to the anatomy and reducing trauma.‘
26th June 2023
All smokers and ex-smokers aged 55-74 will have their risk of cancer assessed in the England’s first-ever national lung cancer screening programme.
The programme will be based on the Targeted Lung Health Check (TLHC) programme, which has been piloted in parts of England.
Under the plans, which will cost £270m annually once fully implemented, GP records will be used to identify patients for screening.
The first phase of the lung cancer screening scheme will reach 40% of the eligible population by March 2025, with the aim of 100% coverage by March 2030, the Government’s announcement said.
Patients will have their risk of cancer assessed based on their smoking history and other factors and those considered high risk will be invited for specialist scans every two years.
It is estimated the rollout will mean 325,000 people will be eligible for a first scan each year with 992,000 scans expected per year in total.
The UK National Screening Committee recommended in November that all four nations in the UK should implement a national lung cancer screening programme.
It said the TLHC programme would be a ‘practical starting point’ for implementation in England while a UK-wide programme needed ‘more modelling’.
During the pilots, approximately 70% of the screening took place in mobile units to ‘ensure easy access’ and ‘focused on more deprived areas where people are four times more likely to smoke’.
Almost 900,000 people were invited for checks, 375,000 risk assessments made and 200,000 scans were carried out.
Of these, more than 2,000 people were detected as having lung cancer, with 76% identified at an earlier stage compared to 29% identified outside of the pilot programme in 2019.
Urging patients receiving an invitation for lung cancer screening to go to their GP and take it up, NHS chief executive Amanda Pritchard said: ‘The NHS lung trucks programme is already delivering life-changing results, with people living in the most deprived areas now more likely to be diagnosed at an earlier stage, giving them a better chance of successful treatment.’
Health secretary Steve Barclay said: ‘Through our [lung cancer] screening programme we are now seeing more diagnoses at stage 1 and stage 2 in the most deprived communities, which is both a positive step and a practical example of how we are reducing health inequalities.
‘Rolling this out further will prolong lives by catching cancer earlier and reducing the levels of treatment required not just benefiting the patient but others waiting for treatment.
‘I am determined to combat cancer on all fronts through better prevention, detection, treatment and research.‘
Cancer Research UK’s chief executive, Michelle Mitchell, said: ‘This is really positive news for a cancer type that takes more lives than any other. Targeted lung screening across England could diagnose people most at risk at an earlier stage, when treatment is more likely to be successful.
‘For the screening programme to succeed, the UK Government must ensure that sufficient diagnostic equipment and staff are in place – a comprehensive and fully-funded NHS workforce plan for England will be vital to this.
‘Given smoking is the leading cause of lung cancer, it’s good to see that smoking cessation will be part of the programme. This needs to be embedded across all sites and stop smoking services must be properly funded to ensure people can quit smoking for good.
‘Other UK nations now need to follow suit to ensure everyone eligible can benefit from these potentially lifesaving lung checks.‘
9th June 2023
Skin cancer diagnosis will be achieved faster and closer to homes as the NHS is set to accelerate the rollout of teledermatology across specialist services and GP practices.
The diagnostic technology, which involves taking high resolution images of spots, moles or lesions on people’s skin, uses a small lens the size of a 50p piece attached to a phone camera. Known as a dermatoscope, it enables specialist dermatologists to double the number of patients they can review in a day.
Currently used in about 15% of trusts offering dermatology services, teledermatology is set to be rolled out to all areas of the country by July this year.
The use of dermatoscopes is also being expanded across GP practices and community diagnostic centres, which can support people living in rural communities to get a faster diagnosis without having to travel for a specialist appointment or avoid the requirement to attend a specialist.
This means people will be able to start treatment more quickly, helping to reduce waiting times and improving chances of survival.
Some hospitals are seeing almost all patient’s diagnosed and treated for skin cancer within two months of an urgent GP referral thanks to teledermatology, according to the NHS.
According to the NHS, more than 600,000 people have been referred for skin cancer checks in the last year – almost a tenth (9%) higher than in the previous year and double the number sent for checks almost a decade ago. Over 56,000 patients with skin cancer received treatment last year.
According to Cancer Research UK, There could be around 26,500 new cases of melanoma skin cancer every year in the UK by 2038-2040, projections suggest.
Announcing the rollout, NHS chief executive Amanda Pritchard said: ‘There is no denying that increased demand has placed huge pressure on services, but championing the use of digital technology and new ways of working is key to reducing waits and is exactly why we are accelerating the use of teledermatology – it is a small piece of kit that has the potential to speed up diagnosis and treatment for tens of thousands with skin cancer.’
Somerset GP Dr Tom While said: ‘Being able to get a swift and specialist opinion on a skin lesion or rash, and advice on treatment or local surgical options, often negates the need to refer the patient on to another hospital to see the specialist in person. This not only reduces waiting lists, but strongly benefits my patients who live in rural areas, saving them from long unnecessary journeys.
‘If a patient does need to be referred on to a specialist, then the teledermatology service helps to streamline that process, ensuring the patient is seen in the correct clinic at the right time.’
Magnifying lenses that use artificial intelligence technology to identify the presence of cancerous skin lesions are also being trialled by the NHS.
Ms Pritchard highlighted that the technology is ‘proving highly effective in areas that have trialled the technology so far’, with 10,000 unneeded face-to-face appointments avoided so far.
The technology, known as Deep Ensemble for the Recognition of Malignancy (DERM), is initially being used alongside clinician assessments, but it is hoped it will provide both faster and more accurate skin cancer detection.
Neil Daly, founder and CEO of Skin Analytics – the company behind the DERM technology – said: ‘Our mission is to help more people survive skin cancer and by providing easier access to skin cancer assessments and we are excited to help additional hospitals see more patients faster through the use of our DERM technology.
‘The NHS’s decision to roll out DERM to support teledermatology is another positive example of the NHS championing world-leading technologies and the next generation of dermatology pathways.’
30th May 2023
Heart attack diagnosis could be achieved quicker and more accurately via the use of a new artificial intelligence algorithm developed by the University of Edinburgh.
Tested on 10,286 patients in six countries, the algorithm – named CoDE-ACS – was able to rule out a heart attack in more than double the number of patients compared to guideline-recommended pathways, and with an accuracy of 99.6%.
The tool was also found to identify those whose abnormal troponin levels were due to a heart attack rather than another condition.
It performed well regardless of age, sex or pre-existing health condition, which the authors say reduces the potential for misdiagnosis and inequalities across the population.
Funded by the British Heart Foundation (BHF) and the UK’s National Institute for Health and Care Research, the study was published in the journal Nature Medicine.
Its lead author, Professor Nicholas Mills, BHF professor of cardiology at the Centre for Cardiovascular Science, University of Edinburgh, said: ‘For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives. Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straight forward. Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy emergency departments.’
CODE-ACS was developed using data from 10,038 patients in Scotland who had arrived at hospital with a suspected heart attack. It uses routinely collected patient information, such as age, sex, ECG findings and medical history, as well as troponin levels, to predict the probability that an individual has had a heart attack. The result is a probability score from 0 to 100 for each patient.
Clinical trials are now underway in Scotland to assess whether the AI tool can help doctors reduce pressure on overcrowded emergency departments.
11th May 2023
Dr Sumeet Hindocha has a passion for artificial intelligence, with his work focusing on radiomics and deep learning in lung cancer. He speaks to Hospital Healthcare Europe about his latest research and the uses and considerations of AI-enabled diagnostics in medicine.
Dr Sumeet Hindocha is a clinical oncology specialist registrar at The Royal Marsden NHS Foundation Trust and a researcher in artificial intelligence (AI). He is currently leading the trust’s OCTAPUS-AI study to investigate how this technology can help identify which patients with non-small cell lung cancer are at higher risk of recurrence.
Lung cancer is the leading cause of cancer deaths worldwide. Non-small cell lung cancer (NSCLC) is behind almost 85% of cases and is often curable when detected early enough. Radiotherapy is a key treatment modality for it, but, unfortunately, recurrence can occur in over a third (36%) of patients treated with radiotherapy.
We know that the earlier we detect recurrence the better the outcomes generally are for patients. It means we can get them on to the next line of treatment or offer the best support as soon as possible. This could reduce the impact the disease has on their lives and help patients live longer.
The aim of our study is to see whether AI could help identify the risk of cancer returning in these patients using CT scans. The study addresses the National Institute of Healthcare and Clinical Excellence’s call for further research into using prognostic factors to develop risk-stratification models to inform optimal surveillance strategies after treatment for lung cancer.
Artificial intelligence has had a big impact in improving various aspects of our lives and work, from automating routine tasks to even things like the programmes recommended to us on Netflix or smart home devices like Siri or Alexa. What’s really exciting about its application in healthcare is its significant potential to improve patient outcomes and experience. We have a huge amount of data from imaging and electronic patient records that can be readily applied to AI. It gives us the ability to detect patterns of disease that would otherwise be difficult to uncover, to develop new drugs and even streamline how we deliver healthcare.
Researchers from the Institute of Cancer Research, Imperial College London and the Early Diagnosis and Detection Centre, which aims to accelerate early diagnosis of cancer and is supported by funding from the Royal Marsden Cancer Charity and the National Institute for Health and Care Research.
We compared different models of machine learning (ML) – a type of type of AI that enables computer software to learn complex data patterns and automatically predict outcomes – to determine which could most accurately identify NSCLC patients at risk of recurrence following curative radiotherapy.
Anonymised, routinely available clinical data from 657 NSCLC patients treated at five UK hospitals was used to compare different ML algorithms based on various prognostic factors such as age, gender and the tumour’s characteristics on scans to predict recurrence and survival at two years from their treatment. We then developed and tested models to categorise patients into low and high risk of recurrence, recurrence-free survival and overall survival.
A patient’s tumour size and stage, the type and intensity of radiotherapy, and their smoking status, BMI and age were the most important clinical factors in the final AI model’s algorithm for predicting patient outcomes.
The results suggested that this technology could be used to help personalise, and therefore improve, the surveillance of patients following treatment based on their risk. This could lead to recurrence being detected earlier in high-risk patients, ensuring that they receive urgent access to the next line of treatment that could potentially improve their outcomes.
In this phase, as well as clinical data, we used imaging data describing the tumours’ characteristics – a technique known as radiomics – taken from radiotherapy treatment planning CT scans on over 900 NSCLC patients in the UK and Netherlands.
Radiomic data can also be linked with biological markers. We believe it could be a useful tool in both personalising medicine and improving post-treatment surveillance. This data was used to develop and test ML models to see how accurately they could predict recurrence.
The TNM staging system, which describes the amount and spread of cancer in a patient’s body, is the current gold standard in predicting prognosis. However, our model was found to better correctly identify which NSCLC patients were at a higher risk of recurrence within two years of completing radiotherapy than a model built on the TNM staging system.
We are at an early stage, and there’s a lot more work to do before we have a tool ready for use in the clinic. However, our results suggest that our AI model could be better at predicting tumour regrowth than traditional methods. This means that, using our technology, clinicians may eventually be able to identify which patients are at a higher risk of recurrence and offer them more targeted follow up. If recurrence did occur, this would be detected earlier so patients could be offered the next line of treatment as soon as possible. Meanwhile, low-risk patients could potentially be spared unnecessary follow-up scans and hospital visits.
This is also an exciting project because we don’t have to put patients through extra procedures for the model to work, as the data is routinely collected during the course of their normal treatment. Furthermore, in theory, there’s no reason why we can’t adapt the same tool to predict recurrence for other cancers.
So far, we’ve looked at CT scans and clinical data. We know from other areas of research [see next question] that some models have been developed using other patient data, for instance previous biopsy results or blood markers.
The next stage would look to improve the performance of the algorithm with more advanced AI techniques, such as deep learning or multimodal approaches, that incorporate different forms of data. Once the model is optimised, the next stage would likely be a prospective study to see if it can accurately predict risk of recurrence in patients currently starting radiotherapy treatment.
Our group has published a review paper that provides an overview of how AI is being used across the spectrum of cancer care, from screening and diagnosis through to treatment and follow up. We explore its implementation in primary care, radiology, pathology and oncology.
AI application in healthcare data has the potential to revolutionise early cancer diagnosis and may provide support for capacity concerns through automation. It can also allow us to effectively analyse complex data from many modalities, including clinical text, genomic, metabolomic and radiomic data.
In the review, we discuss myriad convolutional neural network – or CNN – models that can detect early-stage cancers on scan or biopsy images with high accuracy. Some had a proven impact on workflow triage. Many commercial solutions for automated cancer detection are becoming available, and we are likely to see increasing adoption in the coming years.
One of the biggest challenges facing healthcare right now is increasing demand, more complex cases and a shortage of workers. AI could augment our workflow, not replacing people, but doing some of the easier jobs so staff can focus on the more challenging tasks.
In the setting of patient decision-support, caution is needed to ensure that models are robustly validated before use.
In our review, we also highlight several challenges around the implementation of AI, including data anonymisation and storage, which can be time-consuming and costly for healthcare institutions.
We also discuss model bias, including the under-reporting of important demographic information, such as race and ethnicity, and the implications this can have on generalisability.
In terms of how study quality and model uptake can be improved going forwards, quality assurance frameworks, such as SPIRIT-AI, and methods to standardise radiomic feature values across institutions, as proposed by the image biomarker standardisation initiative, may help. Moreover, disease-specific, gold-standard test sets could help clinicians benchmark multiple competing models more readily.
Despite the above challenges, the implications of AI for early cancer diagnosis are highly promising, and this field is likely to grow rapidly in the coming years.
3rd May 2023
NHS England has tasked hospitals with turning around diagnostic test results for suspected cancer within 10 days.
Hundreds of patients who have been referred under the urgent pathway will receive faster news about whether they have cancer or not helping to reduce anxiety and start treatment more quickly, NHS England said.
A letter sent to local health leaders has also asked teams to prioritise diagnostic tests like MRI scans for cancer in community diagnostic centres (CDCs) or to free up capacity within hospitals by moving elective activity into the centres.
Earlier this month, figures showed more than 42% of patients are waiting more than 62 days for their first cancer treatment from urgent GP referral.
It follows a report from the Public Accounts Committee in March which warned that cancer waiting times are at their worst ever level and NHS England was unlikely to meet its recovery target of moving back to 85% treated within 62 days of referral.
But the latest figures did show some improvement in two week wait times from the previous month with 86% of people seen by a specialist within a fortnight of urgent referral up from 81%.
In February, NHS England said it achieved the faster diagnosis standard for suspected cancer for the first time, with three quarters of those referred receiving a definitive diagnosis or all clear within 28 days – 171,453 people.
There has been high demand for services with up to one in four GP referrals a month for cancer.
In March 2022 to Feb 2023, 470,000 more people were checked for cancer compared with the same period before the pandemic, the figures show.
There are now 105 CDCs in place and offering a ‘one stop shop’ for tests, NHS England confirmed.
Dame Cally Palmer, NHS national director for cancer, said: ‘It is a testament to the hard work of NHS staff that we are seeing and treating record numbers of patients for cancer, and have made significant progress bringing down the backlog and achieving the target for diagnosing three quarters of people within 28 days – all despite huge demand and pressures on the system.
‘Fortunately, the vast majority of suspected cancer patients waiting for a diagnostic test will not have cancer, but for those waiting it can be a very anxious time, so we are asking trusts to aim for a 10-day turnaround time between GP referral and tests results for patients – so we can get people the all-clear faster, or in some cases ensure patients diagnosed with cancer are able to start treatment sooner.’
Professor Mike Osborn, president of the Royal College of Pathologists, said: ‘We welcome the announcement of support for pathology services which will help our members provide the quicker diagnoses that patients need.
‘Pathologists have long asked for improvements in digital pathology and infrastructure to help them provide better patient care. We fully support this initiative and the fresh focus on pathology which it should provide will, we hope, make a real difference to patients.’
This news story was originally published by our sister publication Pulse.
2nd November 2020
Current COVID-19 diagnostics rely on a PCR test, which can only be undertaken via laboratory analysis and, in some cases, the result is not available for a few days. Now researchers from the UK and Germany have reported their preliminary findings on the use of a device that analyses a breath sample to identify infected patients within 10 minutes. The study makes use of exhaled volatile organic compounds (VOCs) in breath which are subjected to gas chromatography and either mass spectrometry (GC-MS) or ion mobility spectrometry. The researchers based their study on the fact that there are distinct breath biochemistry derangements in respiratory illness and that this could be utilised for the detection of those infected with COVID-19 compared to other viral illnesses. The study recruited participants who presented with respiratory symptoms consistent with COVID-19 and this was confirmed with a PCR test. Participants then exhaled through a disposable tube and a sample of breath withdrawn and placed in the GC-MS.
A total of 98 patients were recruited of whom 79% had confirmed COVID-19. Differentiation of COVID-19 from other conditions was possible in 81.5% of patients. Exhaled breath compounds were attributed to a combination ketosis, impaired gastrointestinal function and inflammatory responses. A distinct panel of compounds including ethanal, octanal, acetone, butanone, methanol, heptanal and one unidentified compound, provided the basis to rule in COVID-19.
The authors reported that the instrument can be easily used in emergency departments for a quick assessment of whether a patient has COVID-19 and that the sampling technique does not pose a risk for clinicians performing the task. They called for further and larger studies to validate these preliminary findings.
Ruszkiewicz DM et al. Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry – a feasibility study. EClinical Medicine 2020; https://doi.org/10.1016/j.eclinm.2020.100609