Cancer Imaging (May 2024)

Prognostic and predictive value of interstitial lung abnormalities and EGFR mutation status in patients with non-small cell lung cancer

  • Xiaoli Xu,
  • Min Zhu,
  • Zixing Wang,
  • Jialu Li,
  • Tao Ouyang,
  • Cen Chen,
  • Kewu Huang,
  • Yuhui Zhang,
  • Yanli L. Gao

DOI
https://doi.org/10.1186/s40644-024-00712-9
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 9

Abstract

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Abstract Background To determine the predictive value of interstitial lung abnormalities (ILA) for epidermal growth factor receptor (EGFR) mutation status and assess the prognostic significance of EGFR and ILA in patients with non-small cell lung cancer (NSCLC). Methods We reviewed 797 consecutive patients with a histologically proven diagnosis of primary NSCLC from January 2013 to October 2018. Of these, 109 patients with NSCLC were found to have concomitant ILA. Multivariate logistic regression analysis was used to identify the significant clinical and computed tomography (CT) findings in predicting EGFR mutations. Cox proportional hazard models were used to identify significant prognostic factors. Results EGFR mutations were identified in 22 of 109 tumors (20.2%). Multivariate analysis showed that the models incorporating clinical, tumor CT and ILA CT features yielded areas under the receiver operating characteristic curve (AUC) values of 0.749, 0.838, and 0.849, respectively. When combining the three models, the independent predictive factors for EGFR mutations were non-fibrotic ILA, female sex, and small tumor size, with an AUC value of 0.920 (95% confidence interval[CI]: 0.861–0.978, p < 0.001). In the multivariate Cox model, EGFR mutations (hazard ratio = 0.169, 95% CI = 0.042–0.675, p = 0.012; 692 days vs. 301 days) were independently associated with extended overall survival compared to the wild-type. Conclusion Non-fibrotic ILA independently predicts the presence of EGFR mutations, and the presence of EGFR mutations rather than non-fibrotic ILA serves as an independent good prognostic factor for patients with NSCLC.

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