Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

Our plain-English summary of Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial, by M.W. Marcus, S.W. Duffy, A. Devaraj, et al (Thorax, 2019).

The study

• Authors: M.W. Marcus, S.W. Duffy, A. Devaraj, B.A. Green, M. Oudkerk, D. Baldwin, J. Field
• Journal: Thorax
• Published: 2019;74(8):761-767
• Type of article: Randomized trial
• Cited by: 29 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

Background: Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.

Methods: Data from the UK Lung Cancer Screening trial were analysed. Multivariable logistic regression models were used to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline and at 3-month and 12-month repeat screening.

Results: Of 1994 participants who underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median follow-up of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of cancer, early and late onset of family history of lung cancer, smoking duration, FVC, nodule type (pure ground-glass and part-solid) and volume as measured by semiautomated volumetry. The final model incorporating all predictors had excellent discrimination: area under the receiver operating characteristic curve (AUC 0.885, 95% CI 0.880 to 0.889). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC 0.882, 95% CI 0.848 to 0.907). The risk model had a good calibration (goodness-of-fit χ[8] 8.13, p=0.42).

Conclusions: Our model may be used in estimating the probability of lung cancer in nodules detected at baseline and at 3 months and 12 months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programmes.

Trial Registration Number: 78513845.

© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

In plain English

This study analysed the UK Lung Cancer Screening trial, a randomised pilot of low-dose CT screening in high-risk people aged 50 to 75. Of 1994 participants scanned, 1013 had 5063 lung nodules, and 52 (2.6%) developed lung cancer over a median 4 years of follow-up. The authors modelled the chance that a nodule seen at the first scan or at 3- or 12-month repeat scans was cancer. Asbestos exposure, recorded as yes or no without fiber type or dose, was one factor considered. In the final model it had an odds ratio of 1.801 (95% CI 1.252 to 2.591). In the simple comparison, though, 26.9% of participants with malignant nodules reported occupational asbestos exposure, against 38.1% with benign nodules (p=0.269). Other predictors included bronchitis, family history of lung cancer, smoking duration, and nodule type and volume. The model's discrimination score (AUC) was 0.885 (95% CI 0.880 to 0.889), and 0.882 (95% CI 0.848 to 0.907) after internal validation. The authors concluded it may help estimate cancer probability in such nodules, but said it needs testing in an independent population.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Development and internal validation of a lung cancer risk prediction model (multivariable logistic regression, analysed per nodule), using data from the CT arm of a multicentre randomised controlled pilot trial of low-dose CT screening
• Who was studied: People aged 50 to 75 in Liverpool, Knowsley, Sefton, Cambridgeshire, Peterborough and Bedfordshire (UK). Of 247,354 approached, 4055 high-risk individuals (estimated risk 5% or more over 5 years) were randomised and 2028 were assigned to CT, of whom 1994 were scanned. The analysis covered the 1013 participants with nodules (5063 nodules), with a median follow-up of 4 years. Ethical approval was given in 2010; recruitment dates are not stated.
• Exposure: Asbestos exposure (labelled in Table 3 as occupational exposure to asbestos, recorded as yes or no) was one of many candidate predictors. No fiber type, job or dose is given. Other candidates were age, gender, BMI, respiratory diseases, history of cancer, family history of lung cancer, smoking duration, lung function, and nodule volume, type, location and count.
• Main results:
  • Of the 1994 participants who underwent CT, 1013 had a total of 5063 lung nodules, and 52 (2.6%) developed lung cancer during a median follow-up of 4 years.
  • In the final multivariable model, asbestos exposure was associated with malignancy in a nodule: OR 1.801 (95% CI 1.252 to 2.591), p=0.002.
  • In the descriptive comparison (Table 3), occupational asbestos exposure was reported by 366 (38.1%) of participants with benign nodules and 14 (26.9%) of participants with malignant nodules. The p value for this comparison was 0.269, which is not statistically significant. Asbestos was not among the factors listed as significant in the univariate analysis.
  • Other factors in the final model included early-onset family history of lung cancer (OR 7.378, 95% CI 4.834 to 11.262), late-onset family history (OR 4.818, 95% CI 3.220 to 7.209), bronchitis (OR 5.823, 95% CI 3.894 to 8.704) and non-solid nodule type compared with solid nodules (OR 5.153, 95% CI 2.662 to 9.976).
  • Asthma had an inverse association with malignancy in the final model (OR 0.459, 95% CI 0.305 to 0.693), which the authors described as a protective association.
  • The model's discrimination (ability to classify correctly) was AUC 0.885 (95% CI 0.880 to 0.889). After internal validation by bootstrap resampling with correction for optimism, it was 0.882 (95% CI 0.848 to 0.907).
  • The Hosmer-Lemeshow goodness-of-fit test gave chi-squared (8 degrees of freedom) 8.13, p=0.42, which the authors described as excellent calibration.
• The authors' conclusion: The authors concluded that their model may be used to estimate the probability of lung cancer in nodules detected at baseline and at 3 and 12 months, allowing more efficient stratification of follow-up in population-based screening programmes.
• Limitations the authors state:
  • Spiculation was not included in the model because few nodules had this feature, and the effect of volume doubling time could not be examined.
  • The model was developed in a cohort at particularly high risk of lung cancer and may perform less well in lower-risk populations.
  • The model was validated only internally; the authors stated that the ultimate test is validation in an independent population.
  • Geographical variation in lung cancer rates means the model needs evaluation in geographically diverse populations.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer.

Medical terms in this study, explained in our Lexicon: Low-Dose CT Screening, Lung Cancer, CT Scan, Asbestos Exposure.

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