Evidence map›Paper›PMID 42264884›Full record

Observational studyBMJ open respiratory research2026

Observational study of predictors and outcomes of lung cancer in never-smokers in the UK (OLIVE): study protocol.

Sindhu Bhaarrati Naidu, Gabrielle Guvensen, Honghan Wu, Rumana Z Omar, Usha Menon, Sanjay Popat, David A Moore, Yiwen Soo, Amyn Bhamani, Ricky Thakrar and 5 more

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in BMJ open respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Sindhu Bhaarrati NaiduUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK sindhu.naidu@nhs.net.ORCID http://orcid.org/0000-0002-1106-6545
Gabrielle GuvensenUniversity College London, London, UK.
Honghan WuSchool of Health and Wellbeing, University of Glasgow, Glasgow, UK.
Rumana Z OmarDepartment of Statistical Science, UCL, London, UK.
Usha MenonMRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, UCL, London, UK.
Sanjay PopatLung Unit, The Royal Marsden Hospital, London, England, UK.
David A MooreCRUK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK.
Yiwen SooLungs for Living Research Centre, UCL Respiratory, University College London, London, UK.ORCID http://orcid.org/0009-0007-2649-2401
Amyn BhamaniUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK.ORCID http://orcid.org/0000-0002-8575-6119
Ricky ThakrarUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK.
Tanya AhmadDepartment of Medical Oncology, Cancer Division, University College London Hospitals, London, UK.
Olive Ppi GroupUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK.
Sam M JanesUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK.
Martin D ForsterCRUK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK.
Neal NavaniUCL Respiratory, University College London Lungs for Living Research Centre, London, England, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLung cancer is commonly associated with smoking. However, if considered separately, lung cancer in never-smokers (LCINS) is the seventh most common cause of cancer-related death worldwide. Expanding the limited understanding of LCINS, especially in the UK, is crucial to improving diagnosis. METHODS AND ANALYSIS: We will establish a retrospective and prospective UK multicentre observational cohort comprising never-smoking (lifetime use of <100 tobacco cigarettes) adults with lung cancer using routinely available primary and secondary care electronic health records (EHRs). Demographic data including occupation and ethnicity, exposures and lung cancer outcomes will be extracted. Deep learning-based natural language processing will be used to analyse free-text data. Quantitative or qualitative data collection for information not available in EHR may be initiated in the future. Follow-up will be until death, withdrawal or end of study (5 years from study activation date). Primary outcomes will be sociodemographic characteristics, comorbidities, environmental exposures, pathways to presentation and symptoms. A feasibility pilot of 225 patients (25 participants per year both retrospectively and prospectively) will be undertaken at one hospital before extending the study to multiple sites. Patient representatives have been involved in adapting the research protocol including the recruitment process and patient-facing materials. ETHICS AND DISSEMINATION: National ethical approval has been obtained from Health Research Authority (HRA) (24/YH/0147). Access to patient records without explicit consent, including deceased patient records, is legally supported by the Confidentiality Advisory Group and HRA. The study is sponsored by University College London Hospital.Data will be anonymised and analysed 2 years after the study start date and on study completion. Together with our patient representatives, we will disseminate data to the scientific and academic community and public through peer-reviewed publications, presentations at conferences, patient-facing charities and social media.

Indexed as

Lung NeoplasmsNon-SmokersAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesResearch DesignRetrospective StudiesUnited KingdomClinical EpidemiologyLung CancerLung Cancer ChemotherapyNon-Small Cell Lung Cancer

Identifiers

PMID42264884
PMCPMC13264864

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.