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Diagnostic value of circulating chromogranin a for neuroendocrine tumors: a systematic review and meta-analysis.

Yang X, Yang Y, Li Z, Cheng C, Yang T, Wang C, Liu L, Liu S - PLoS ONE (2015)

Bottom Line: In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.8962.These data demonstrate that circulating CgA is an efficient biomarker for the diagnosis of NETs with high sensitivity and specificity, which indicates that it may be helpful for the clinical management of NETs.However, further studies are needed to clarify this issue.

View Article: PubMed Central - PubMed

Affiliation: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

ABSTRACT

Background: In previous decades, chromogranin A (CgA) has been demonstrated to be the most promising biomarker for the diagnosis of neuroendocrine tumors (NETs), but its diagnostic value is still controversial. This meta-analysis aimed to estimate the potential diagnostic value of circulating CgA for NETs.

Methods: We collected relevant studies from several electronic databases as well as from reference lists. Diagnostic indices of CgA were pooled with random effects models. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves for the diagnosis of NETs were used to estimate the overall diagnostic efficiency.

Results: Through a search strategy, 13 studies met the inclusion criteria and were included. These studies contained 1260 patients with NETs and 967 healthy controls in the total sample. As a result, the overall sensitivity, specificity and diagnostic odds ratio (DOR) were 0.73 (95% CI: 0.71 to 0.76), 0.95 (95% CI: 0.93 to 0.96) and 56.29 (95% CI: 25.27 to 125.38), respectively, while the summary positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.8962.

Conclusions: These data demonstrate that circulating CgA is an efficient biomarker for the diagnosis of NETs with high sensitivity and specificity, which indicates that it may be helpful for the clinical management of NETs. However, further studies are needed to clarify this issue.

No MeSH data available.


Related in: MedlinePlus

The forest plots show the pooled diagnostic indices of CgA for NETs.The heterogeneity caused by the non-threshold effect is quantified by inconsistency (I2). Because this heterogeneity exists, a random effects model was used to pool these data. The point efficiencies from each study are shown as squares, and the pooled efficiencies are shown as diamonds. The degree of freedom is abbreviated as df. As shown, A) the pooled sensitivity and specificity are 0.73 (95% CI: 0.71 to 0.76) and 0.95 (95% CI: 0.93 to 0.96), respectively. B) the pooled PLR and NLR are 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. C) the pooled DOR is 56.29 (95% CI: 25.27 to 125.38).
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pone.0124884.g002: The forest plots show the pooled diagnostic indices of CgA for NETs.The heterogeneity caused by the non-threshold effect is quantified by inconsistency (I2). Because this heterogeneity exists, a random effects model was used to pool these data. The point efficiencies from each study are shown as squares, and the pooled efficiencies are shown as diamonds. The degree of freedom is abbreviated as df. As shown, A) the pooled sensitivity and specificity are 0.73 (95% CI: 0.71 to 0.76) and 0.95 (95% CI: 0.93 to 0.96), respectively. B) the pooled PLR and NLR are 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. C) the pooled DOR is 56.29 (95% CI: 25.27 to 125.38).

Mentions: An examination of the potential sources of heterogeneity is indispensable for any meta-analysis before the pooling the data from the included studies into summary assessments. [35]. The determination of the heterogeneity within the studies is crucial to the comprehension of the potential factors that have a great effect on accuracy assessments and on the appraisal of the appropriateness of statistical pooling of the diagnostic accuracy from the various studies [21]. One of the primary and important causes of heterogeneity in the diagnostic accuracy of tests is the threshold effect. To assess whether the heterogeneity of CgA from the threshold effect exists in the included studies, we first used a Spearman test to calculate the correlation coefficient and P value between the logit of sensitivity and logit of 1-specificity. As a result, the Spearman correlation coefficient was -0.055, and the P value was 0.858 (>0.05), which could exclude the heterogeneity caused by the threshold effect. Another important factor that may contribute to the heterogeneity among the studies is the non-threshold effect. In this meta-analysis, the inconsistency index (I2) was employed to quantify the heterogeneity from the non-threshold effect. The I2 values in the forest plots of the diagnostic indices were more than 50% (as shown in Fig 2), which suggested that heterogeneity caused by the non-threshold effect existed among these studies.


Diagnostic value of circulating chromogranin a for neuroendocrine tumors: a systematic review and meta-analysis.

Yang X, Yang Y, Li Z, Cheng C, Yang T, Wang C, Liu L, Liu S - PLoS ONE (2015)

The forest plots show the pooled diagnostic indices of CgA for NETs.The heterogeneity caused by the non-threshold effect is quantified by inconsistency (I2). Because this heterogeneity exists, a random effects model was used to pool these data. The point efficiencies from each study are shown as squares, and the pooled efficiencies are shown as diamonds. The degree of freedom is abbreviated as df. As shown, A) the pooled sensitivity and specificity are 0.73 (95% CI: 0.71 to 0.76) and 0.95 (95% CI: 0.93 to 0.96), respectively. B) the pooled PLR and NLR are 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. C) the pooled DOR is 56.29 (95% CI: 25.27 to 125.38).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403810&req=5

pone.0124884.g002: The forest plots show the pooled diagnostic indices of CgA for NETs.The heterogeneity caused by the non-threshold effect is quantified by inconsistency (I2). Because this heterogeneity exists, a random effects model was used to pool these data. The point efficiencies from each study are shown as squares, and the pooled efficiencies are shown as diamonds. The degree of freedom is abbreviated as df. As shown, A) the pooled sensitivity and specificity are 0.73 (95% CI: 0.71 to 0.76) and 0.95 (95% CI: 0.93 to 0.96), respectively. B) the pooled PLR and NLR are 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. C) the pooled DOR is 56.29 (95% CI: 25.27 to 125.38).
Mentions: An examination of the potential sources of heterogeneity is indispensable for any meta-analysis before the pooling the data from the included studies into summary assessments. [35]. The determination of the heterogeneity within the studies is crucial to the comprehension of the potential factors that have a great effect on accuracy assessments and on the appraisal of the appropriateness of statistical pooling of the diagnostic accuracy from the various studies [21]. One of the primary and important causes of heterogeneity in the diagnostic accuracy of tests is the threshold effect. To assess whether the heterogeneity of CgA from the threshold effect exists in the included studies, we first used a Spearman test to calculate the correlation coefficient and P value between the logit of sensitivity and logit of 1-specificity. As a result, the Spearman correlation coefficient was -0.055, and the P value was 0.858 (>0.05), which could exclude the heterogeneity caused by the threshold effect. Another important factor that may contribute to the heterogeneity among the studies is the non-threshold effect. In this meta-analysis, the inconsistency index (I2) was employed to quantify the heterogeneity from the non-threshold effect. The I2 values in the forest plots of the diagnostic indices were more than 50% (as shown in Fig 2), which suggested that heterogeneity caused by the non-threshold effect existed among these studies.

Bottom Line: In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.8962.These data demonstrate that circulating CgA is an efficient biomarker for the diagnosis of NETs with high sensitivity and specificity, which indicates that it may be helpful for the clinical management of NETs.However, further studies are needed to clarify this issue.

View Article: PubMed Central - PubMed

Affiliation: Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

ABSTRACT

Background: In previous decades, chromogranin A (CgA) has been demonstrated to be the most promising biomarker for the diagnosis of neuroendocrine tumors (NETs), but its diagnostic value is still controversial. This meta-analysis aimed to estimate the potential diagnostic value of circulating CgA for NETs.

Methods: We collected relevant studies from several electronic databases as well as from reference lists. Diagnostic indices of CgA were pooled with random effects models. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves for the diagnosis of NETs were used to estimate the overall diagnostic efficiency.

Results: Through a search strategy, 13 studies met the inclusion criteria and were included. These studies contained 1260 patients with NETs and 967 healthy controls in the total sample. As a result, the overall sensitivity, specificity and diagnostic odds ratio (DOR) were 0.73 (95% CI: 0.71 to 0.76), 0.95 (95% CI: 0.93 to 0.96) and 56.29 (95% CI: 25.27 to 125.38), respectively, while the summary positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 14.56 (95% CI: 6.62 to 32.02) and 0.26 (95% CI: 0.18 to 0.38), respectively. In addition, the area under the curve (AUC) of the circulating CgA in the diagnosis of NETs was 0.8962.

Conclusions: These data demonstrate that circulating CgA is an efficient biomarker for the diagnosis of NETs with high sensitivity and specificity, which indicates that it may be helpful for the clinical management of NETs. However, further studies are needed to clarify this issue.

No MeSH data available.


Related in: MedlinePlus