Prognostic value of myosteatosis in patients with lung cancer: a systematic review and meta-analysis

Int J Clin Oncol. 2022 Jul;27(7):1127-1138. doi: 10.1007/s10147-022-02181-1. Epub 2022 May 23.

Abstract

The prognostic value of myosteatosis has been widely investigated in lung cancer, yet conclusions remain controversial. The purpose of this meta-analysis was to illuminate this issue. Medline, Embase, Cochrane Library and Web of Science Core Collection online databases were systematically searched from inception to 24 September 2021. Newcastle-Ottawa Scale tool was applied to evaluate the quality of included studies. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and progression-free survival (PFS) were used to examine prognostic value of myosteatosis. Subgroup analysis and sensitivity analysis were conducted to assess heterogeneity and stability of results. A total of 484 articles were screened from which 9 eligible studies involving 1667 patients were enrolled in this meta-analysis. Lung cancer patients with myosteatosis had significantly worse OS than patients without myosteatosis (HR 1.10, 95% CI 1.05-1.16, P < 0.001), both in six multivariate analysis (HR 1.46, 95% CI 1.16-1.85, P = 0.001) and in three univariate analysis (HR 1.08, 95% CI 1.03-1.14, P = 0.003). Pooled data from five studies using multivariate survival analysis also showed that patients with myosteatosis had a statistically significant unfavorable PFS (HR = 1.27, 95% CI 1.00-1.62, P = 0.049). Sensitivity analysis showed the result for OS was stable. But for PFS, the result was not robust. Myosteatosis might serve as an independent indicator of unfavorable survival outcomes for OS and PFS in lung cancer patients. Further studies are needed to confirm our results.

Keywords: Lung cancer; Meta-analysis; Myosteatosis; Prognosis.

Publication types

  • Meta-Analysis
  • Review
  • Systematic Review

MeSH terms

  • Humans
  • Lung Neoplasms* / complications
  • Prognosis
  • Proportional Hazards Models
  • Survival Analysis