SynthesisMolecular diagnosis & therapy2026
Interlesional Heterogeneity of EGFR Mutations: A Systematic Review and Meta-analysis.
Synthesis in Molecular diagnosis & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- Concordance study of bronchial biopsy tissues and matched bronchial brushing cytology specimens in driver gene detection of non-small cell lung cancer.Frontiers in oncology · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundActivating epidermal growth factor receptor (EGFR) mutations are key drivers in non-small cell lung cancer (NSCLC) and other solid tumours, predicting responses to tyrosine kinase inhibitors (TKIs). Tumour heterogeneity alongside sampling and technical factors may contribute to discordant EGFR status across biopsies, complicating treatment decisions. However, systematic evidence on prevalence and drivers of discordance remains limited.
methodsThis systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in PROSPERO (CRD42024615727). MEDLINE, Embase, and Scopus (2004-2024) were searched for studies reporting EGFR mutation discordance in adult solid tumours. Eligible studies compared primary and metastatic tumours (tissue-tissue), tissue and liquid biopsies (tissue-liquid), or different liquid biopsies. Data extraction and QUADAS-2 risk of bias assessment were performed independently. Discordance proportions were analysed on the logit scale with Haldane-Anscombe correction when needed. Random-effects meta-analysis was conducted using the Paule-Mandel estimator with Hartung-Knapp-Sidik-Jonkman confidence intervals. Subgroup analyses and meta-regression were used to estimate pooled discordance and explore potential predictors.
resultsA total of 154 studies (15,560 patients) predominantly involving NSCLC were included. The pooled discordance rate was 16.1% (95% confidence interval 14.2-18.2). Rates were similar for tissue-tissue (16.8%) and tissue-liquid (15.5%), but higher for liquid-liquid (34.0%). Plasma was the most-studied liquid source (16.5%), while cerebrospinal fluid showed the highest discordance (35.5%). Prior TKI exposure was associated with higher discordance (25.8%) compared with treatment-naive patients (14.6%; p = 0.003). Patients who later developed resistance also had higher baseline discordance (21.0% vs 15.0%; p = 0.042). Discordance varied by metastatic site, from 15.1% in lymph nodes to 17.9% in brain/central nervous system. Meta-regression identified TKI exposure, resistance, and mutation prevalence as predictors.
conclusionsEGFR mutation discordance is common and clinically relevant, particularly in NSCLC, but varies substantially by sampling strategy, biofluid, treatment context, and metastatic site. Given the high between-study heterogeneity and the predominance of NSCLC and Asian cohorts, pooled estimates should be interpreted as descriptive summaries rather than universally generalisable benchmarks. These findings support integrated and context-aware sampling strategies for EGFR-targeted therapy and resistance monitoring.
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