ArticleDiscover oncology2026
Novel biomarkers for lung cancer diagnosis in chronic obstructive pulmonary disease: a systematic review and metanalysis.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
introductionChronic Obstructive Pulmonary Disease (COPD) significantly increases the risk of developing lung cancer, yet early detection remains challenging due to overlapping clinical features. Identifying reliable, non-invasive biomarkers for lung cancer diagnosis in this high-risk population could enhance screening strategies and outcomes. MATERIAL AND
methodsWe conducted a systematic review and meta-analysis of studies evaluating the diagnostic accuracy of biomarkers for lung cancer in patients with COPD. Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science for relevant studies up to April 2025. Data were synthesized using random-effects models to estimate pooled area under the curve (AUC) and diagnostic odds ratio (DOR). Subgroup analyses were conducted according to biomarker class. Risk of bias was assessed with the QUADAS-2 tool, and publication bias was evaluated via funnel plots and Egger's test.
resultsSeventeen studies were included, encompassing diverse biomarker categories: proteomics, volatile organic compounds (VOCs), telomere length, oxidative stress, genomics, inflammatory markers, and clinical models. The pooled AUC was 0.82 and the pooled logDOR was 2.76, indicating good overall diagnostic performance. VOCs and telomere length showed the highest pooled accuracy. Despite substantial heterogeneity, sensitivity analyses confirmed the robustness of the findings. Publication bias was present for AUC estimates but not for DOR.
conclusionsThis review highlights promising biomarker classes, particularly VOCs, telomere length, and clinical models, for the early detection of lung cancer in COPD patients. Further multicentre, prospective studies with standardized methodologies are needed to validate these biomarkers and support their integration into clinical practice.
trial registrationPROSPERO IDENTIFIER CRD420251066505.
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