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. 2017 May 11;8(38):64358-64372.
doi: 10.18632/oncotarget.17825. eCollection 2017 Sep 8.

Systematic review and meta-analysis of the efficacy of serum neuron-specific enolase for early small cell lung cancer screening

Lang Huang #  1 , Jian-Guo Zhou #  1 , Wen-Xiu Yao #  2 , Xu Tian  3 , Shui-Ping Lv  1 , Ting-You Zhang  1 , Shu-Han Jin  4 , Yu-Ju Bai  1 , Hu Ma  1
Affiliations

Systematic review and meta-analysis of the efficacy of serum neuron-specific enolase for early small cell lung cancer screening

Lang Huang et al. Oncotarget. .

Abstract

We performed a pooled analysis of the efficacy of serum neuron-specific enolase (NSE) levels for early detection of small cell lung cancer (SCLC) in patients with benign lung diseases and healthy individuals. Comprehensive searches of several databases through September 2016 were conducted. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Ultimately, 33 studies containing 9546 samples were included in the review. Pooled sensitivity of NSE for detecting SCLC was 0.688 (95%CI: 0.627-0.743), specificity was 0.921 (95%CI: 0.890-0.944), positive likelihood ratio was 8.744 (95%CI: 6.308-12.121), negative likelihood ratio was 0.339 (95%CI: 0.283- 0.405), diagnostic odds ratio was 25.827 (95%CI: 17.490- 38.136) and area under the curve was 0.88 (95%CI: 0.85- 0.91). Meta-regression indicated that study region was a source of heterogeneity in the sensitivity and joint models, while cut-off level was a source in the joint model. Subgroup analysis showed that enzyme linked immunosorbent assays had the highest sensitivity and radioimmunoassay assays had the highest specificity. The diagnostic performance was better in Europe [sensitivity: 0.740 (95%CI: 0.676-0.795), specificity: 0.932 (95%CI: 0.904-0.953)] than in Asia [sensitivity: 0.590 (95%CI: 0.496- 0.678), specificity: 0.901 (95%CI: 0.819-0.948)]. In Europe, 25 ng/ml is likely the most suitable NSE cut-off level. NSE thus has high diagnostic efficacy when screening for SCLC, though the efficacy differs depending on study region, assay method and cut-off level. In the clinic, NSE measurements should be considered along with clinical symptoms, image results and histopathology.

Keywords: diagnosis accuracy; meta-analysis; neuron-specific enolase; small cell lung cancer; systematic review.

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Conflict of interest statement

CONFLICTS OF INTEREST There is no conflicts of interest for any author regarding the publication of this manuscript.

Figures

Figure 1
Figure 1. Flow chart of the systematic review process
Figure 2
Figure 2. Risk of bias and applicability concerns summary
Figure 3
Figure 3. Risk of bias and applicability concerns graph
Figure 4
Figure 4. Forest plot estimating the sensitivity of NSE in SCLC patients in the selected studies
(Point estimates for sensitivity and 95% CIs are shown with pooled estimates; NSE = neuron-specific enolase; SCLC = small cell lung cancer; CI = confidence interval; Q = Cochran Q statistic).
Figure 5
Figure 5. Forest plot estimating the specificity of NSE in SCLC patients in the selected studies
(Point estimates for specificity and 95% CIs are shown along with pooled estimates; NSE = neuron-specific enolase; SCLC = small cell lung cancer; CI = confidence interval; Q = Cochran Q statistic).
Figure 6
Figure 6. Bivariate boxplot of sensitivity and specificity in the 33 included trials
Figure 7
Figure 7. Likelihood ratio scattergram evaluating the positive likelihood ratios of NSE in the diagnosis of SCLC
(Point estimates for positive likelihood ratio and 95% CIs are shown along with pooled estimates; NSE = neuron-specific enolase; SCLC = small cell lung cancer).
Figure 8
Figure 8. SROC curve for NSE in the diagnosis of SCLC
(AUC = area under the curve; NSE = neuron-specific enolase; SCLC = small cell lung cancer; SROC = summary receiver-operating characteristic).
Figure 9
Figure 9. Fagan diagram evaluating the overall value of SCLC for the diagnosis of SCLC
(NSE = neuron-specific enolase; SCLC = small cell lung cancer).
Figure 10
Figure 10. Deek's funnel plot evaluating publication bias in the included studies
See this image and copyright information in PMC

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