Frontiers in Medicine (Dec 2023)

Distinguishing EGFR mutant subtypes in stage IA non-small cell lung cancer using the presence status of ground glass opacity and final histologic classification: a systematic review and meta-analysis

  • Jianhao Qiu,
  • Zheng Ma,
  • Rongyang Li,
  • Chenghao Qu,
  • Kun Wang,
  • Binyan Liu,
  • Yu Tian,
  • Hui Tian

DOI
https://doi.org/10.3389/fmed.2023.1268846
Journal volume & issue
Vol. 10

Abstract

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BackgroundThe progression of early stage non-small cell lung cancer (NSCLC) is closely related to epidermal growth factor receptor (EGFR) mutation status. The purpose of this study was to systematically investigate the relationship between EGFR mutation status and demographic, imaging, and ultimately pathologic features in patients with NSCLC.MethodsA complete literature search was conducted using the PubMed, Web of Science, EMBASE, and Cochrane Library databases to discover articles published by May 15, 2023 that were eligible. The relationship between EGFR mutation status and specific demographic, imaging, and ultimately pathologic features in patients with NSCLC was evaluated using pooled odds ratios (ORs) and their 95% confidence intervals (CIs). The standardized mean difference (SMD) with 95% CIs was the appropriate statistic to summarize standard deviations (SDs) means for continuous variables.ResultsA total of 9 studies with 1789 patients were included in this analysis. The final findings suggested that patients with a greater age, female gender, and non-smoking status would have a relatively higher incidence of EGFR mutations. Additionally, the risk of EGFR mutations increased with larger tumor diameter, tumor imaging presentation of mixed ground glass opacity (mGGO), and tumor pathological findings of minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). Significantly, malignancies presenting as MIA are more likely to contain L858R point mutations (OR = 1.80; 95% CI: 1.04–3.13; p = 0.04) rather than exon 19 deletions (OR = 1.81; 95% CI: 0.95–3.44; p = 0.07).ConclusionThis meta-analysis showed that imaging parameters and histological classifications of pulmonary nodules may be able to predict stage IA NSCLC genetic changes.

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