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Press Releases

Take a look at a selection of our recent media coverage:

Single low-dose CT scan helps predict future lung cancer risk

1st February 2023

Using a single low-dose computed tomography scan and a deep learning model enabled predictions of lung cancer risk over one to six years

US and Taiwanese researchers have shown that the use of a single low-dose computed tomography (CT) scan, together with a deep learning algorithm, allows for a prediction of an individual’s risk of lung cancer over the next six years.

The use of low-dose CT screening has been shown to reduce mortality from lung cancer. Such screening allows for the early detection of the disease and hence the potential for better patient outcomes, although it has been suggested that the current screening guidelines might overlook vulnerable populations with a disproportionate lung cancer burden. Nevertheless, the efficiency of lung cancer screening could be improved by individualising the assessment of future cancer risk. The problem is determining how this can achieved. To date, there are some data to support the use of clinical risk assessment models that incorporate various factors compared to simply using age and cumulative smoking exposure. However, there are enormous possibilities created by greater use of artificial intelligence and deep learning models. In fact, it has become possible to utilise low-dose CT scan results and the presence of pulmonary nodules, into a model and to therefore optimise the screening process. But how useful are other pieces of information gathered from a CT scan beyond the presence of nodules, and could this other information be used by a deep learning model to predict future cancer risk.

This was the aim of the current study in which researchers developed a model, which they termed ‘Sybil’ using the entire volumetric low-dose CT data, without clinical and demographic information, to predict an individual’s future cancer risk. Sybil was able to run in the background of a radiology reading station and did not require annotation by a radiologist. The model was validated using information from three independent screening datasets which included individuals who were non-smokers.

Low-dose CT screening and lung cancer risk prediction

In total, data were retrieved from over 27,000 patients held in three separate databases. Sybil achieved an area under the curve (AUC) of 0.92, 0.86 and 0.94, for the 1-year prediction of lung cancer for each of these datasets. In addition, the concordance indices over 6 years were 0.75, 0.81 and 0.80 for the same three data sets.

The authors concluded that Sybil was able to accurately predict individual’s future risk of lung cancer based on a single low-dose CT scan and called for further studies to better understand Sybil’s clinical application.

Mikheal PG et al. Sybil: a validated Deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. Clin Oncol 2023

Lung cancer risk is 10-fold higher in smokers who are not recommended for annual screening

4th August 2022

The lung cancer risk of both current light and former heavy smokers for whom screening with a computer tomography (CT) scan is not currently recommended appears to be 10-folder higher than those who have never smoked according to the findings of a study by US researchers.

The World Health Organization estimates that across the globe in 2020, there were 2.21 million cases of lung cancer and which led to 1.8 million deaths. Thus attempts to screen for the early signs of lung cancer might potentially reduce the number of lung cancer deaths. The development of low-dose helical computed tomography (CT) scanning has shown that low-dose CT enables the detection of many lung cancer tumours at an early stage. In fact, a recent trial concluded that among high-risk individuals who underwent CT screening, lung-cancer mortality was significantly lower compared to those who did not undergo screening. Consequently, screening recommendations with low-dose computed tomography (LDCT) have been produced and suggest annual screening for adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. Screening, however, is not advocated for former smokers with a 20 pack-year or greater smoking history who quit 15 or more years ago (former heavy smokers) or for current smokers with a smoking history of 20 pack-years or less (current non-heavy smokers). The reasons for excluding these two groups is not clear but presumably is related to an expected lower lung cancer risk. But how valid is this recommended exclusion? This was the basis for the present study in which researchers sought to examine the level of cancer risk among these two groups for whom screening is not recommended.

The researchers used patient data from the Cardiovascular Health Study which enrolled nearly 6,000 community-dwelling older adults (65 years and older) although their analysis was restricted to individuals who were free of cancer at enrolment and for whom pack-year smoking history and smoking cessation data were available. The main outcome of interest was incident lung cancer during follow-up.

Lung cancer risk over time

A total of 4279 participants with a mean age of 72.8 years (57.3% female) were included and followed for a median of 13.3 years. There were 861 current non-heavy smokers and 615 former heavy smokers and 1,973 never smokers who were used as the reference point.

During follow-up, lung cancer occurred in 0.5% of never smokers, 5% of current non-heavy smokers and 5% of former heavy smokers.

The age-adjusted hazard ratio (HR) for incident lung cancer for current non-heavy smokers was 10.06 (95% CI 3.41 – 29.70) and 10.22 (95% CI 4.86 – 21.50) for former heavy smokers, i.e., the two groups for whom screening is not recommended. The mortality risk for current, non-heavy smokers was 53% higher (HR = 1.53, 95% CI 1.22 – 1.92) and 18% higher (HR = 1.18, 95% CI 1.05 – 1.32) for former heavy smokers.

The authors concluded that there appears to be a very high lung cancer risk among those who are excluded from the recommendations for CT screening and called for future studies to examine whether annual screening could reduce lung cancer mortality in these populations.

Faselis C et al. Assessment of Lung Cancer Risk Among Smokers for Whom Annual Screening Is Not Recommended JAMA Oncol 2022

Standalone AI reader could significantly reduce radiologist workload in lung cancer screening

13th January 2022

A standalone AI reader outperformed four experienced radiologists in CT lung cancer screening and could reduce their workload by over 80%

Using a standalone artificial intelligence (AI) reader for lung cancer screening with ultra low-dose computed tomography (ULDCT) could potentially reduce the workload of radiologists by over 80%. This was the conclusion of a study by a team from the Department of Epidemiology, University of Groningen, Groningen, The Netherlands.

Lung cancer was responsible for 2.21 million cases and 1.8 million deaths in 2020 and volume-based, low-dose CT screening of high-risk patients has been shown to significantly reduce lung-cancer mortality compared to those who underwent no screening. Moreover, low-dose CT lung cancer screening has become an evidence-based reality. However, the introduction of such screening will undoubtedly create an enormous increase in the workload of radiologists and while the use of a standalone AI as a second reader for lung cancer screening with CT has shown much promise, how well an AI system could perform as a standalone system remains uncertain.

For the present study, the Dutch researchers, sought to evaluate the performance of a standalone AI as an impartial reader in ULDCT lung cancer baseline screening compared to that of experienced radiologists and a consensus read reference standard. They used a dataset of CT scans from participants who underwent a baseline scan and who were found to have at least one solid nodule of any size in their scan. Other inclusion criteria for the study were: participants aged 50 – 80 years, > 30 pack-years smoking history, current or former smoker and those who did not develop lung cancer within two years of their baseline scan. All of the participant’s scans were independently analysed by five thoracic radiologists and the standalone AI then independently analysed all the scans to detect, measure and classify nodules. In addition, an independent consensus read was performed by a panel of three, experienced radiologists and who sought to determine the number of positive misclassifications (PM) and negative misclassifications (NM). A PM was classified as nodules > 100 cubic mms and NM < 100 cubic mms. The results from the 5 radiologist reader and the standalone AI were compared to the consensus read to determine the number of PM and NM results as well as the number of discrepancies.


A total of 283 participants with a mean age of 64.6 years (56.9% male) with a total of 1149 lung nodules were analysed.

The consensus read was 83 PMs and 200 NMs and the standalone AI had 61 discrepancies (53 PM and 8 NM) compared to a total of 43, 36, 29, 28 and 50 from the five respective radiologists. From these results, the authors calculated that when using a standalone AI as the main reader for general lung cancer screening, there would be a workload reduction of between 77.4% and 86.7%.

The authors concluded that a standalone AI could significantly reduce the workload of radiologists in lung cancer screening.


Lancaster HL et al. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification. Lung Cancer 2022