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11th May 2023
The Essex Cardiothoracic Centre celebrates its 16-year anniversary this summer, and with a recent funding injection leading to the development of a new cardiac catheter lab, the centre is gearing up to support more patients requiring thoracic procedures in the county.
The Essex Cardiothoracic Centre (CTC), located at Basildon Hospital and part of Mid and South Essex NHS Foundation Trust, serves a population of 1.8 million people across the county.
Each year, approximately 900 cardiac surgeries, 500 thoracic operations and close to 2,400 cardiology procedures are performed.
The CTC recently received £2.3m in national funding, which has been spent on a new 60 square metre cardiac catheter lab, expected to open in May 2023. The development will free up theatre space for thoracic surgery and increase lung cancer surgery numbers by a third.
Hospital Healthcare Europe caught up with Michael Catling, deputy director of operations for the Essex Cardiothoracic Centre and Mid and South Essex Cardiology, to find out more.
Pre-2007, patients who needed heart or lung treatment had to travel to London as there was poor provision for these services in the county. The CTC was set up following a national requirement to increase capacity for cardiac surgery and specialist interventional cardiology procedures. The Department of Health selected Basildon as the site for a new Essex centre.
Our core capacity for thoracic surgery will increase by around 150 cases annually as heart procedures that were previously carried out in the hybrid theatre will be moved to the new cath lab, freeing up space.
We perform around 500 thoracic surgeries per annum of which around 180 are lung cancer cases. Demand has risen over the past five years, and until now, we have been reliant upon ad hoc additional lists, often taking capacity from cardiac surgery, which is not sustainable.
The ability to have an extra thoracic list every week in core capacity on the schedule without having to take any other service away releases the pressure and gives us the capacity to meet increasing demand in the coming years.
We provide a wide range of thoracic services for conditions of the lung, airway, pleura, mediastinum, chest wall and diaphragm. These incorporate cancer and benign disease with both open and minimally invasive approaches.
The main driver is improvements in diagnostic services for lung cancer, including targeted screening, which increases the detection of early-stage cancers. Local community and hospital services are shortening diagnostic pathways and increasing access to diagnostics.
One of our communities within Essex was in the first wave of the national Targeted Lung Health Check (TLHC) programme that launched in November 2020. In April 2022, this rolled out to a second location and current plans are to extend this to all populations.
As of March, at least nine people in Southend had previously undiscovered lung cancer found and treated thanks to the TLHC. The same programme is also benefiting residents living in Thurrock. The latest data shows that 4,834 checks have been completed in Southend.
In addition, there has been a total of 1,827 CT scans and 127 referrals into hospitals after those scans to follow up care linked to cardiovascular disease, gallbladder, respiratory, breast, gastro, urology, liver and renal findings.
A 16-bed cardiothoracic ITU, 32-bed cardiothoracic surgery ward, 28-bed cardiology ward, cardiothoracic theatre suite with four theatres including a hybrid IR theatre, cath lab suite including EP (electrophysiology) and structural labs, cardiac imaging including CT, MRI, special echo, outpatient suite, cardiac rehab department, pulmonary physiology department and overnight facilities for on-call teams and for relatives.
We offer a wide range of specialist tertiary services. Our higher volume procedures within cardiology are PCI (percutaneous coronary intervention), cardiac pacing, TAVI (transcatheter aortic valve implantation) and EP. Within the surgical service these are CABG (coronary artery bypass graft), AVR (aortic valve replacement), MVR (mitral valve repair), and complex aortic.
Additionally, over the past three years there has been a move from open lung surgery to minimally invasive lung surgery and our programme includes VATS (video-assisted thoracoscopic surgery).
Around 600. This includes consultant cardiologists, surgeons, anaesthetists, thoracic histopathologists, specialist nursing teams, cardiac physiologists, clinical perfusion scientists, surgical care and operating department practitioners.
We also have UK training posts and international clinical fellows and have developed a strong national and international reputation for research within the centre. This is supported by our excellent relationship with the Anglia Ruskin University Medical School faculty with which we have a research fellow programme.
We have several colleagues with roles in external organisations and learned societies such as the British Cardiovascular Intervention Society. They contribute regularly to national and international events including presentations at Society for Cardiothoracic Surgery 2023, and by providing live-streamed cath lab cases for the 2023 physiology course at the Cardiovascular Centre in Aalst, Belgium.
We’re very focused on doing what we do well, developing genuine tertiary services and particularly looking after our talented and passionate staff through training opportunities.
As a centre we are committed to offering excellent specialist training both to our own specialists and to related teams across Essex. The surgical team has delivered a programme of eight clinical simulation training events and wet labs over the past 12 months.
This has included two specifically related to the thoracic surgery service. In November 2022, we ran a lung resection day covering the teaching of both anaesthetic and surgical perspective with attendees from several other major tertiary centres in the UK.
In January 2023, we conducted a chest drain insertion day run jointly with the respiratory team, including hands-on simulation. Other courses in the past year have covered CABG, aortic and mitral valve surgery. This helps with staff development and retention.
As a specialist centre serving a large population and a number of local hospitals, our clinical plans incorporate continued development of specialist interventions and increased collaboration with local hospitals and primary care teams. This will include introduction of mitral TEER (transcatheter edge-to-edge repair), minimally invasive cardiac surgery, complex aortic surgery and thoracic port surgery techniques. We will be extending multi-disciplinary team working in chronic long-term conditions such as heart failure and atrial fibrillation through virtual ward models supported by latest technologies.
Our most important asset is our workforce, and we will continue to invest in training and education for our teams as well as developing new extended scope roles for advanced care practitioners.
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.
20th December 2022
Widespread state medical marijuana legalisation in the US is associated with a lower rate of opioid dispensing and pain-related hospital events among some adults receiving treatment for newly diagnosed cancer according to an analysis by US researchers.
Pain is an extremely common cancer symptom with a 2022 meta-analysis of 12 studies (10 with breast cancer and 2 lung cancer) patients, finding a pooled pain prevalence rate of 40%. Although paracetamol and non-steroidal anti-inflammatory drugs are universally accepted as part of the treatment of cancer pain at any stage of the WHO analgesic ladder, strong opioids are the mainstay of analgesic therapy in treating moderate to severe cancer-related pain. Nevertheless, with tightened regulations leading to a decrease in opioid prescribing across the United States, evidence points to a decline in opioid use among end-of-life care in those with cancer although there has been a rise in pain-related emergency department visits, suggesting that end of life cancer pain management may be worsening. Although medical marijuana has been studied and found to be efficacious for relief of pain in patients with advanced cancer pain not fully relieved by strong opioids, a 2016 review suggested that while marijuana may have the potential for refractory cancer pain, much of the data are based on animal data, small trials, or are outdated.
With the potential to help patients with cancer pain, in the current study, US researchers set out to assess the associations between medical marijuana legalisation and opioid-related and pain-related outcomes for adult patients receiving cancer treatment. The team used data from national commercial claims between 2012 to 2017. The researchers assessed several measures including the proportion of patients having 1 or more days of opioids and 1 or more pain-related emergency department visits or hospital events, during the 6 months after a new cancer diagnosis.
Medical marijuana and opiate use
A total of 38,189 patients with newly diagnosed breast cancer, 12,816 with colorectal cancer (55.4% male) and 7,190 (51.1% female) with lung cancer were included in the analysis.
Medical marijuana legalisation was associated with a reduction in the rate of 1 or more opioid days from 90.1% to 84.4% (difference = 5.6, 95% CI 2.2 – 9.0, p = 0.01) among breast cancer patients. For colorectal cancer patients, there was also a reduction, this time from 89.4% to 84.4% (difference = 4.9, 95% CI 0.5 – 9.4, p = 0.03). Finally, opioid use reduced from 31.5% to 22.1% (difference = 9.4, 95% CI 0.8 – 17.9, p = 0.03) among patients with lung cancer with recent opioids.
Medical marijuana legalisation was also associated with a reduction in the rate of 1 or more pain-related hospital events from 19.3% to 13.0% (difference = 6.3, 95% CI 0.70 – 12.0, p = 0.03) among patients with lung cancer with recent opioids. However, the difference for the other two forms of cancer was not significant.
The authors concluded that medical marijuana legalisation was associated with a lower rate of opioid dispensing and pain-related hospital events among some adults receiving treatment for newly diagnosed cancer.
Citation
Bao Y et al. Medical Marijuana Legalization and Opioid- and Pain-Related Outcomes Among Patients Newly Diagnosed With Cancer Receiving Anticancer Treatment. JAMA Oncol 2022
23rd September 2022
The exposure to particulate matter derived from air pollution represents a mechanism through which lung cancer can develop among individuals who have never smoked according to the findings of research presented at the European Society for Medical Oncology (ESMO) Congress 2022 by scientists of the Francis Crick Institute and University College London.
Globally in 2020 there were an estimated 2.21 million cases of lung cancer and 1.80 million deaths. There are two primary forms of lung cancer, small cell lung cancer and non-small cell lung cancer (NSCLC) with this latter form accounting for approximately 84% of all cases. It has been recognised for several years that particulate matter in outdoor air pollution with a size of at least 2.5 micrometers, leads to an 18% higher risk of lung cancer among those who had never smoked. However, the mechanisms driving this increased risk among those who do not smoke has remained unclear.
In the study presented at the ESMO congress, researchers focused on lung cancers due to a mutation in the epidermal growth factor receptor (EGFR), which is a transmembrane receptor tyrosine kinase protein, expressed in some normal epithelial, mesenchymal, and neurogenic tissue. Moreover, research suggests that EGFR protein expression is a risk factor in patients with NSCLC. Using normal lung tissue samples from humans and mice, the team investigated the consequences of increasing 2.5um particulate matter (PM2.5) concentrations with cancer risk.
Particulate matter exposure and cancer risk
Samples were analysed from 463,679 individuals and the team found that increasing PM2.5 levels were associated with a greater risk for EGFR mutated NSCLC samples from England, South Korea and Taiwan. This was also associated with an increased risk of mesothelioma (hazard ratio, HR = 1.19), lung (HR = 1.16), anal (HR = 1.23), small intestine (HR=1.30), glioblastoma (HR=1.19), lip, oral cavity and pharynx (HR = 1.15) and laryngeal carcinomas (HR = 1.26) in UK Biobank samples, for each 1 ug/m3 PM2.5 increment.
A further interesting finding was the presence of EGFR driver mutations in 18% of normal lung samples and a further mutation (KRAS) in 33% of samples. The team also showed that PM promoted a macrophage response and a progenitor-like state in lung epithelium harbouring mutant EGFR. Consistent with particulate matter promoting NSCLC in at-risk epithelium harbouring driver mutations, PM increased tumour burden in three EGFR or KRAS driven lung cancer models in a dose-dependent manner.
In a press release discussing their findings, Charles Swanton who presented the findings at ESMO, said ‘We found that driver mutations in EGFR and KRAS genes, commonly found in lung cancers, are actually present in normal lung tissue and are a likely consequence of ageing. In our research, these mutations alone only weakly potentiated cancer in laboratory models. However, when lung cells with these mutations were exposed to air pollutants, we saw more cancers and these occurred more quickly than when lung cells with these mutations were not exposed to pollutants, suggesting that air pollution promotes the initiation of lung cancer in cells harbouring driver gene mutations. The next step is to discover why some lung cells with mutations become cancerous when exposed to pollutants while others don’t.‘
6th May 2022
The detection of emphysema via visual or quantitative assessment on a CT-scan has been found to be linked with a higher odds of developing lung cancer. This was the conclusion of a systematic review by researchers from the Departments of Epidemiology, Radiology and Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
The World Health Organization reported that in 2020 there were 2.21 million cases of lung cancer which resulted in 1.8 million deaths. A chest computer tomography (CT) scan enables quantification of the amount of emphysema present in the lungs and while some evidence suggests that emphysema on a CT scan is related to lung cancer in a high-risk population, other data indicates that no CT measures of emphysema have an independent association with lung cancer.
With some uncertainty over the association between the presence of emphysema seen on a CT scan and lung cancer, for the present study, the researchers decided to undertake a systematic review and meta-analysis to further probe this association. They searched all the major databases and included studies that specifically assessed the association between emphysema and the diagnosis of lung cancer based on histopathologic examination. The team defined visual emphysema as disrupted lung vasculature and parenchyma with low attenuation occupying any lung zone on the chest CT scan and quantitative emphysema as the percentage of total lung volume below a given Hounsfield unit threshold (-950 HU at full inspiration). They also sought to examine whether the severity of emphysema was associated with lung cancer and graded this as trace, mild, moderate/severe. The studies were stratified based on whether visual or quantitative assessments were used and the presence of confirmed lung cancer was the main outcome of interest expressed and expressed as an odds ratio, adjusted for age, gender and smoking status.
Emphysema and lung cancer risk
A total of 21 studies met the inclusion criteria with 3907 patients who had lung cancer and 103,175 controls.
The pooled odds ratio (OR) for lung cancer in the presence of emphysema was 2.3 (95% CI 2 – 2.6) in studies which employed visual assessment and 2.2 (95% CI 1.8 – 2.8) where the authors used quantitative assessment.
When stratified by disease severity, the overall pooled OR for lung cancer increased with disease severity although there were differences based on whether the data was acquired by visual or quantitative assessment. For example, in studies that employed visual assessment, the ORs for lung cancer were 2.5 (trace disease), 3.7 (mild disease) and 4.5 (moderate to severe disease). While these odds ratios were still elevated based on quantitative assessments, the magnitudes were slightly lower e.g., 1.9 for trace disease and 2.5 (moderate to severe disease).
Based on their findings, the author concluded that the presence of emphysema diagnosed on a chest CT scan was independently associated with a higher odds of developing lung cancer.
Citation
Yang X et al. Association between Chest CT–defined Emphysema and Lung Cancer: A Systematic Review and Meta-Analysis Radiology 2022
20th January 2022
Smoking cessation at the time of a lung cancer diagnosis is linked to an improved survival from both non-small and small cell lung cancer, according to the findings of a systematic review by a team from the Institute for Cancer Research, Prevention and Clinical Network, Florence, Italy
Data from the World Health Organization shows that in 2020, globally, there were 2.21 million cases of lung cancer and which led to 1.8 million deaths. In addition, lung cancer has a poor prognosis and Cancer Research UK suggests that only around 15% of those with lung cancer will survive for 5 years or more after diagnosis. Cigarette smoking is a major factor in the development of lung cancer, with one analysis of the burden of respiratory tract cancers indicating that smoking contributed to an estimated 64·2% of all deaths from tracheal, bronchus, and lung cancer and 63·4% of all deaths from larynx cancer in 2019.
Although one study with 517 smokers, found that smoking cessation at the time of a lung cancer diagnosis can reduce the risk of future lung cancer, for the present study, the Italian team sought to provide a more robust estimate of the overall prognostic value of smoking cessation at or around the time of a lung cancer diagnosis. They searched for articles which included those who continued to smoke and those who quit in relation to their cancer diagnosis and the associated changes in survival. The team calculated relative risks for the association between smoking cessation and the survival from lung cancer.
Findings
A total of 21 studies were included in the systematic review with patients diagnosed with non-small cell lung cancer (10 studies, 5315 patients) and small cell lung cancer (5 studies, 1133 patients), together with a further six studies of both cancer subtypes or where the subtype was not specified. The mean age of lung cancer diagnosis across the studies ranged from 60 to 70 years and the proportion of men ranged from 40.2% to 91.8%. The duration of follow-up also ranged from 12 months to 27.7 years.
Smoking cessation at or around the time of diagnosis was associated with a better overall survival regardless of lung cancer type. For smoking cessation at any time, compared to those who continued smoking (used as the reference group), the relative risk for non-small cell lung cancer was 0.77 (relative risk, RR = 0.77, 95% CI 0.66 – 0.90) and this reduction was broadly similar compared to those stopping strictly at or after their diagnosis or up to 12 months before the diagnosis. For small cell lung cancer, overall survival was also broadly similar (RR = 0.75, 95% CI 0.57 – 0.99). Even in studies where the cancer subtype was not specified, there were survival benefits among quitters (RR = 0.81, 95% CI 0.68 – 0.96).
The authors calculated an overall benefit for those who undertook smoking cessation at or around the time of their lung cancer diagnosis, finding that such individuals had a 29% improvement in their overall survival compared to those who continued to smoke (RR = 0.71, 95% CI 0.64 – 80).
The authors concluded that advice to quit smoking at or around the time of a lung cancer diagnosis, should arguably become a non-optional part of the management of these patients.
Citation
Caini S et al. Quitting smoking at or around diagnosis improves the overall survival of lung cancer patients: a systematic review and meta-analysis J Thorac Oncol 2022
20th September 2021
According to Cancer Research UK, there are around 47,800 new lung cancer cases each year and approximately 35,100 deaths, which equates to 96 deaths every day. Furthermore, Cancer Research UK estimates that 79% of lung cancer cases in the UK are preventable with 72% caused by smoking. With such a high incidence of not only cases, but more importantly, preventable cases, there is an urgent need for effective screening methods, especially among individuals who are deemed at high risk such as smokers. In a 1999 study, a low computed-tomography (CT scan) was shown to greatly improve the likelihood of detecting small, non-calcified nodules and hence lung cancer, at an earlier and hence more curable stage. Moreover, subsequent studies have also demonstrated a reduction in lung cancer mortality among those undergoing a low dose CT scan.
With the value of CT screening already firmly established, a UK-based team have published their own findings of a trial comparing the effect of a low dose CT scan compared to usual care, in high-risk patients. The UK lung cancer screening (UKLS) trial, randomised patients to low dose CT screening or usual care, i.e., with no CT scan and was undertaken at two thoracic hospitals in the UK. Eligible patients, aged 50 to 75 years, were those deemed to be at a high risk of developing lung cancer over the next 5 years defined by a risk score of at least 4.5% based on the Liverpool Lung Project risk model (LLPv2). This model includes several possible risk factors such as gender, age, smoking status, smoking duration, family history of lung cancer. Included patients were then randomised to the intervention group (CT scan) or usual care although given the nature of the intervention, blinding was not possible. The primary outcome was mortality due to lung cancer, defined as a death during the follow-up period where lung cancer was listed as an underlying cause. In an effort to provide further evidence, the researchers also undertook a meta-analysis of other recent trials and included their own data, to get a more robust estimate of the benefits of CT scanning.
Findings
A total of 1987 and 1981 individuals were randomised to the CT scan and control arm respectively and followed for a median of 7.3 years. The median age at consent was 68 years (25% female) and among the CT scan group, 38% were current smokers, of whom, 93% had smoked for more than 20 years. During the follow-up period, 76 lung cancers were detected, 30 in the CT scan arm and 46 in the control arm although this difference was not significant (relative risk, RR = 0.65, 96% CI 0.41 – 1.02, p = 0.062). Furthermore, there were no significant differences between the sexes. In addition, there were 512 deaths from any cause and again there was no significant difference between the groups (p = 0.315).
When these results were added to a meta-analysis of 9 randomised, controlled trials, low dose CT scan screening was associated with a 16% relative reduction in lung cancer mortality compared with no screening (RR = 0.84, 95% CI 0.76 – 0.92).
The authors concluded that while their trial had not demonstrated a statistically significant reduction in lung cancer mortality, when their data was combined with other studies, the pooled estimate was significant and provided further support for lung cancer screening via a low dose CT scan.
Citation
Field JK et al. Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis. Lancet Regional health Europe 2021
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