Abstract IA17: Risk prediction modeling in lung cancer: How can we improve?
Authors: Field John K. , Marcus Michael W. 
Published: May 2019

Screening for lung cancer

The results of the US National Lung Screening Trial (NLST) were published in 2011 and are considered a landmark event in lung cancer research. This randomised study of 53,454 individuals showed that computed tomography (CT) scans are able to reduce lung cancer mortality by 20% through early detection, although with important cost and morbidity due to overdiagnosis and treatment of benign nodule. A number of European pilot trials have reported, we await the NELSON, which is the only statistically powered screening trial in Europe. There are now discussions on how to implement lung cancer screening throughout the world, within differing health care systems. The success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention.

Risk prediction models

Thus accurate selection of high-risk individuals for lung cancer screening requires robust methods for risk prediction. The discriminative performance of a risk model depends not only on the identification of individual risk factors, but also on the influence of these risk variables in the presence/absence of other variables, how accurately these factors can be measured, and the appropriateness of the population and statistical techniques used for modeling. However, the main practical application of a risk prediction model is its use by non-specialists for selection of suitable high-risk people for lung cancer screening/intervention. In addition, to being technically detailed and accurate, a risk model needs to be sufficiently user-friendly to be applied in the general population and/or primary care setting. In practical terms, this means that the risk variables should be straightforward to elicit, and the algorithm should be simple to run.

Current lung cancer prediction models

The Lung cancer risk prediction models which have been developed include Bach, Spitz, LLP and more recently the PLCO [1] and EPIC model. The UK Lung cancer Screening trial (UKLS) [2] has been the only RCT trial to date, to select high risk individuals from a population based study for a screening trial, utilising a validated risk prediction model [3]. The data already analysed from the UKLS population based approach will provide valuable information as to how to we should implement lung cancer screening, if it becomes a national programme.

Utilisation of LLPv2 risk model on UKLS screening trial

The LLPv2 risk model has been used to select high-risk individual in the UKLS. UKLS is a randomised controlled trial of LDCT for lung cancer screening, following the Wald single-screen design. In short, the UKLS randomised subjects based on their ≥5% risk of developing lung cancer in the next five years. Using this selection criterion shows that screening programme can potentially be more cost-effective if it is limited to the high-risk segment of the population [2].

Risk models to evaluate indeterminate nodules [4, 5]

The basis of lung cancer CT screening is to identify lung nodules, which are at a level of suspicion whereby they are referred to a specialist clinical team for work-up and potential surgical intervention. It has been demonstrated that such nodules detected within screening trials are often very early stage disease and thus these patients have a very good clinical outcome. However, nodules are common in the scans of many patients, and experienced radiologists using volumetric techniques can now measure these nodules and determine whether they are growing. The major clinical problem concerns nodules which are less than 10mm in diameter or &lt;500mm3 volume is that they potentially require multiple repeat scans to determine the management.

The data from these trials has already started to change the management of indeterminate CT screen detected nodules, thus proving the power of risk prediction modeling in lung cancer, which will contribute to the methodology currently under discussion on how to implement lung cancer CT screening programmes [6].

1. Tammemagi, M.C., et al., Selection criteria for lung-cancer screening. N Engl J Med, 2013. 368(8): p. 728-36.

2. Field, J.K., et al., The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess, 2016. 20(40): p. 1-146.

3. Raji, O.Y., et al., Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case-control and cohort validation study. Ann Intern Med, 2012. 157(4): p. 242-50.

4. McWilliams, A., et al., Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013. 369(10): p. 910-9.

5. Horeweg, N., et al., Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol, 2014. 15(12): p. 1332-41.

6. Field, J.K., et al., CT screening for lung cancer: Is the evidence strong enough? Lung Cancer, 2016. 91: p. 29-35.

Citation Format: John K. Field, Michael W. Marcus. Risk prediction modeling in lung cancer: How can we improve? [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA17.