Publication

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

Marcus, M. W., Duffy, S. W., Devaraj, A., Green, B. A., Oudkerk, M., Baldwin, D. & Field, J., Aug-2019, In : Thorax. 74, 8, p. 761-767 7 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Marcus, M. W., Duffy, S. W., Devaraj, A., Green, B. A., Oudkerk, M., Baldwin, D., & Field, J. (2019). Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial. Thorax, 74(8), 761-767. https://doi.org/10.1136/thoraxjnl-2018-212263

Author

Marcus, Michael W ; Duffy, Stephen W ; Devaraj, Anand ; Green, Beverley A ; Oudkerk, Matthijs ; Baldwin, David ; Field, John. / Probability of cancer in lung nodules using sequential volumetric screening up to 12 months : the UKLS trial. In: Thorax. 2019 ; Vol. 74, No. 8. pp. 761-767.

Harvard

Marcus, MW, Duffy, SW, Devaraj, A, Green, BA, Oudkerk, M, Baldwin, D & Field, J 2019, 'Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial', Thorax, vol. 74, no. 8, pp. 761-767. https://doi.org/10.1136/thoraxjnl-2018-212263

Standard

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months : the UKLS trial. / Marcus, Michael W; Duffy, Stephen W; Devaraj, Anand; Green, Beverley A; Oudkerk, Matthijs; Baldwin, David; Field, John.

In: Thorax, Vol. 74, No. 8, 08.2019, p. 761-767.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Marcus MW, Duffy SW, Devaraj A, Green BA, Oudkerk M, Baldwin D et al. Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial. Thorax. 2019 Aug;74(8):761-767. https://doi.org/10.1136/thoraxjnl-2018-212263


BibTeX

@article{3ec071aaa2604246bb141c6646aa3162,
title = "Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial",
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 = "Marcus, {Michael W} and Duffy, {Stephen W} and Anand Devaraj and Green, {Beverley A} and Matthijs Oudkerk and David Baldwin and John Field",
note = "{\circledC} Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.",
year = "2019",
month = "8",
doi = "10.1136/thoraxjnl-2018-212263",
language = "English",
volume = "74",
pages = "761--767",
journal = "Thorax",
issn = "0040-6376",
publisher = "BMJ PUBLISHING GROUP",
number = "8",

}

RIS

TY - JOUR

T1 - Probability of cancer in lung nodules using sequential volumetric screening up to 12 months

T2 - the UKLS trial

AU - Marcus, Michael W

AU - Duffy, Stephen W

AU - Devaraj, Anand

AU - Green, Beverley A

AU - Oudkerk, Matthijs

AU - Baldwin, David

AU - Field, John

N1 - © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

PY - 2019/8

Y1 - 2019/8

N2 - 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.

AB - 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.

U2 - 10.1136/thoraxjnl-2018-212263

DO - 10.1136/thoraxjnl-2018-212263

M3 - Article

VL - 74

SP - 761

EP - 767

JO - Thorax

JF - Thorax

SN - 0040-6376

IS - 8

ER -

ID: 93430587