Article Text

other Versions

PDF

Correspondence
CT screening for lung cancer
  1. Robert P Young,
  2. Raewyn J Hopkins
  1. Schools of Biological Sciences and Health Sciences, University of Auckland, Auckland, New Zealand
  1. Correspondence to Dr Robert P Young, Director, Respiratory Genetics Group, Schools of Biological Sciences and Health Sciences, University of Auckland, PO Box 26161, Epsom 1344, Auckland, New Zealand; roberty{at}adhb.govt.nz

Statistics from Altmetric.com

We read with interest the recent opinion piece by Field et al1 outlining plans for a CT screening trial in the United Kingdom (the UK Lung Screen (UKLS)) following the results of the National Lung Cancer Screening Trial. We agree that cost-effectiveness and defining who would most likely benefit from CT screening remain key issues to be resolved before CT screening can be offered routinely in clinical practice.2

First, cost-effectiveness is most likely to be achieved through optimising the risk assessment of those potentially eligible for CT screening1 and maximising the number of cancers identified for each scan done. While historical data may assist in this risk assessment,2 it is possible that biomarkers are required to better stratify this risk. In this regard, we and others have shown that a reduced forced expiratory volume in one second (FEV1) is the single most important risk factor (and biomarker) for lung cancer susceptibility and is present in up to 80% of those diagnosed with lung cancer.3 We hypothesise that targeting those smokers with mildly or moderately reduced FEV1 may help maximise picking up of ‘treatable’ lung cancer.3 Such an approach was reported in a small community-based study where lung cancer was detected in 6% of those who underwent baseline CT screening,4 much greater (by over threefold) than that reported by the National Lung Cancer Screening Trial and estimated in the UKLS (1–2%).2 In the absence of abnormal lung function, other biomarkers such as gene-based risk stratification5 might have utility in identifying those at the greatest risk of lung cancer. We note that although neither lung function nor DNA sampling contributes to the Liverpool Lung Cancer Risk Prediction Model,2 all UKLS participants will have these taken.2

Second, apart from optimising entry into a CT-based screening programme, cost-effectiveness might also be improved by limiting subsequent CT screening according to the risk profile. In this regard, we hypothesise that smokers with normal lung function, no evidence of emphysema on baseline CT scan and/or ‘low gene-based risk’5 might not require yearly scanning. Such a group might defer scanning (or increase the scanning interval), much like colonoscopy for bowel cancer screening is individualised according to the risk level.

Both these hypotheses could be examined in the UKLS where the ‘single screen’ design and DNA sampling enable a gene-based risk model to be examined with respect to predictability and survival (figure 1). We conclude that optimisation of patient selection and scan interval, through biomarker-based risk stratification, may help improve the cost-effectiveness of CT screening.

Figure 1

Proposed study design to assess cost-effectiveness in the UK Lung Screen using spirometry and gene-based risk stratification to optimise lung cancer detection rate. LLP, Liverpool Lung Project model.2

References

View Abstract

Footnotes

  • Funding RPY, and his research, is supported by grants from the University of Auckland, Health Research Council of New Zealand and Synergenz Bio Sciences Ltd.

  • Competing interests None.

  • Provenance and peer review Not commissioned; internally peer reviewed.

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Linked Articles

  • PostScript
    John Field David Baldwin Kate Brain Anand Devaraj Tim Eisen Stephen W Duffy David M Hansell Keith Kerr Richard Page Mahash Parmar David Weller David Whynes Paula Williamson
  • PostScript
    Naveen Dutt Deepak T Hari