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Oke JL, Pickup LC, Declerck J, et al. Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol. Diagn Progn Res. 2018;2:22doi: 10.1186/s41512-018-0044-3.
Oke, J. L., Pickup, L. C., Declerck, J., Callister, M. E., Baldwin, D., Gustafson, J., Peschl, H., Ather, S., Tsakok, M., Exell, A., & Gleeson, F. (2018). Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol. Diagnostic and prognostic research, 222. https://doi.org/10.1186/s41512-018-0044-3
Oke, Jason L, et al. "Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol." Diagnostic and prognostic research vol. 2 (2018): 22. doi: https://doi.org/10.1186/s41512-018-0044-3
Oke JL, Pickup LC, Declerck J, Callister ME, Baldwin D, Gustafson J, Peschl H, Ather S, Tsakok M, Exell A, Gleeson F. Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol. Diagn Progn Res. 2018 Nov 29;2:22. doi: 10.1186/s41512-018-0044-3. eCollection 2018. PMID: 31093569; PMCID: PMC6460802.
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Diagn Progn Res. 2018 Nov 29;2:22. doi: 10.1186/s41512-018-0044-3. eCollection 2018.

Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol.

Diagnostic and prognostic research

Jason L Oke, Lyndsey C Pickup, Jérôme Declerck, Matthew E Callister, David Baldwin, Jennifer Gustafson, Heiko Peschl, Sarim Ather, Maria Tsakok, Alan Exell, Fergus Gleeson

Affiliations

  1. 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, OX2 6GG, Oxford, UK.
  2. Optellum Ltd, Oxford, UK.
  3. 3St James' University Hospital, Leeds, UK.
  4. 4Nottingham University Hospitals, Nottingham, UK.
  5. 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK.

PMID: 31093569 PMCID: PMC6460802 DOI: 10.1186/s41512-018-0044-3

Abstract

INTRODUCTION: Lung cancer is a common cancer, with over 1.3 million cases worldwide each year. Early diagnosis using computed tomography (CT) screening has been shown to reduce mortality but also detect non-malignant nodules that require follow-up scanning or alternative methods of investigation. Practical and accurate tools that can predict the probability that a lung nodule is benign or malignant will help reduce costs and the risk of morbidity and mortality associated with lung cancer.

METHODS: Retrospectively collected data from 1500 patients with pulmonary nodule(s) of up to 15 mm detected on routinely performed CT chest scans aged 18 years old or older from three academic centres in the UK will be used to to develop risk stratification models. Radiological, clinical and patient characteristics will be combined in multivariable logistic regression models to predict nodule malignancy. Data from over 1000 participants recruited in a prospective phase of the study will be used to evaluate model performance. Discrimination, calibration and clinical utility measures will be presented.

Keywords: Clinical prediction model; Lung cancer; Pulmonary nodules

Conflict of interest statement

In keeping with the Governance Arrangements for Research Ethics Committees (GAfREC) and University Oxford Policy, research undertaken on data collected before formulation of a study, where data are an

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