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Record W4414136107 · doi:10.1136/bmjresp-2025-003298

Differentiating clinically important interstitial lung abnormalities in lung cancer screening

2025· article· en· W4414136107 on OpenAlexaff
Brintha Selvarajah, Amyn Bhamani, Mehran Azimbagirad, Burcu Ozaltin, Ryoko Egashira, Daisuke Yamuda, John McCabe, Nicola Smallcombe, Priyam Verghese, Ruth Prendecki, Andrew Creamer, Jennifer Dickson, Carolyn Horst, Sophie Tisi, Helen Hall, Chuen R Khaw, Monica Mullin, Kylie Gyertson, Anne-Marie Hacker, Laura Farrelly, Anand Devaraj, Arjun Nair, Mariia Yuneva, Neal Navani, Daniel C. Alexander, Rachel C. Chambers, Joanna C. Porter, Allan Hackshaw, Gísli Jenkins, Sam M. Janes, Joseph Jacob

Bibliographic record

VenueBMJ Open Respiratory Research · 2025
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilCRUK Lung Cancer Centre of ExcellenceRoy Castle Lung Cancer FoundationUniversity College London Hospitals NHS Foundation TrustRosetrees TrustUniversity College LondonGrailUK Regenerative Medicine PlatformNational Institute for Health and Care ResearchGarfield Weston FoundationWellcome TrustFrancis Crick InstituteUCLH Biomedical Research CentreCancer Research UK
KeywordsLungLung cancer screeningComorbidityLung cancerMultiorgan failureInterstitial lung disease

Abstract

fetched live from OpenAlex

BACKGROUND: Interstitial lung abnormalities (ILAs) are common incidental findings in lung cancer screening (LCS). However, challenges remain in identifying clinically relevant ILAs as highlighted in a joint statement by a European multidisciplinary task force led by the European Respiratory Society (ERS). To address these challenges, we analysed ILAs identified in one of Europe's largest LCS studies. METHODS: Of 11 635 LCS individuals, 417 screen-detected ILAs were evaluated using a new visual classification system focused on traction bronchiolectasis: non-fibrotic ILA (no traction bronchiolectasis), fibrotic ILA (traction bronchiolectasis in ≤2 lobes); undiagnosed interstitial lung disease (traction bronchiolectasis in >2 lobes). Observer agreement was compared with Fleischner Society ILA classification using Cohen's Kappa. An age, sex and smoking history-matched control group allowed the examination of associations between baseline ILA/UILD and comorbidities, forced vital capacity (FVC), hospitalisations (Student's t-tests) and mortality (univariable and multivariable Cox proportional hazards models). FINDINGS: Our visual ILA classification showed superior interobserver agreement (K=0.76) versus the Fleischner ILA classification (K=0.64). ILA/UILD subjects had more prevalent comorbidities, increasing (vs controls) approximately 10 years prior to ILA/UILD diagnosis. Compared with controls, mortality rates were 6-fold higher for UILD participants and 3-fold higher for fibrotic and non-fibrotic ILA subtypes. On multivariable Cox regression analysis, ILA/UILD presence (HR=4.90, 95% CI =2.36 to 10.10, p<0.001) showed stronger independent associations with mortality than baseline FVC (HR=0.98, 95% CI =0.96 to 1.00, p=0.04). CONCLUSION: We demonstrate a new reproducible classification of clinically important ILA/UILDs in LCS populations. We highlight that FVC shows limited associations with mortality in ILA/UILD subjects. Increased multiorgan comorbidity in ILA/UILD subjects highlights a need for comprehensive early multisystem evaluation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.138
GPT teacher head0.524
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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