Table 1.
Characteristics of studies included in the final meta-analysis of the diagnostic accuracy of NLR and PLR in thyroid tumor.
Fig 1.
Flow chart of articles identified through literature search through data base and others.
Fig 2.
Graphical presentation of the quality of studies.
Fig 3.
The figure presents the assessment of the risk of bias and applicability concerns for included studies based on QUADAS 2 tool.
The “Risk of Bias” section evaluates four domains: Patient Selection, Index Test, Reference Standard, and Flow and Timing. The “Applicability Concerns” section assesses concerns related to Patient Selection, Index Test, and Reference Standard.
Fig 4.
This figure presents forest plots for the sensitivity and specificity of nine studies evaluating a diagnostic accuracy of NLR.
Each individual study is represented by a square, and the pooled sensitivity and specificity indicated by the diamond shape. The horizontal lines indicate the 95% confidence intervals (CIs).
Fig 5.
This figure presents forest plots for the diagnostic likelihood ratio (DLR) of various studies evaluating a diagnostic accuracy of NLR.
Fig 6.
This figure presents forest plots for the diagnostic likelihood ratio (DLR) of various studies evaluating a diagnostic accuracy of NLR.
Fig 7.
This figure displays the Summary Receiver Operating Characteristic (SROC) curve, which represents the diagnostic accuracy of NLR by combining sensitivity and specificity data from the nine included studies.
Table 2.
Subgroup analysis to check the source of heterogeneity for NLR (n = 9).
Fig 8.
Illustrates a Fagan nomogram, a graphical tool used to determine the post-test probability of a condition based on the pre-test probability and the diagnostic test’s likelihood ratios.
Fig 9.
The Deek’s funnel plot asymmetry test was used in our review to assess publication bias.
The analysis showed that the diagnostic odds ratio was symmetrically distributed across the range of sample sizes, indicating that publication bias was very unlikely.
Fig 10.
The figure presents forest plots for the sensitivity and specificity of six studies evaluating a diagnostic accuracy of PLR.
Each individual study is represented by a square, and the pooled sensitivity and specificity indicated by the diamond shape. The horizontal lines indicate the 95% confidence intervals (CIs).
Fig 11.
This figure displays the Summary Receiver Operating Characteristic (SROC) curve, which represents the diagnostic accuracy of PLR by combining sensitivity and specificity data from the six included studies.
Fig 12.
Illustrates a Fagan nomogram, a graphical tool used to determine the post-test probability of a condition based on the pre-test probability and the diagnostic test’s likelihood ratios.
Table 3.
Subgroup analysis to check the source of heterogeneity for PLR (n = 6).