Fig 1.
Flow chart of the selection process for the included studies.
Table 1.
The analytical results of correlations between p53 mutations and p53 overexpression.
Fig 2.
Forest plot of the sensitivity and specificity of IHC-determined p53 overexpression in detecting p53 mutations.
(A) Forest plot showing the sensitivity of IHC-determined p53 overexpression in detecting p53 mutations. (B) Forest plot showing the specificity of IHC-determined p53 overexpression in detecting p53 mutations. Abbreviations: CI, confidence interval.
Fig 3.
Forest plot of the positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) of IHC-determined p53 overexpression in detecting p53 mutations.
(A) Forest plot showing the positive LR of IHC-determined p53 overexpression in detecting p53 mutations. (B) Forest plot showing the negative LR of IHC-determined p53 overexpression in detecting p53 mutations.
Fig 4.
The summary receiver operating characteristic (SROC) curve and the likelihood ratio scattergram for IHC-determined p53 overexpression in the identification of p53 mutations in HCC for all studies.
(A) The SROC curve summarizes the overall diagnostic accuracy of IHC-determined p53 overexpression for the identification of p53 mutations. The size of the dots for 1-specificity and sensitivity of the single studies in the ROC space reflects the sample size (number of patients) in the study. (B) The likelihood ratio scattergram shows the diagnostic performance of IHC-determined p53 overexpression in the identification of p53 mutations. Q* = point at which sensitivity and specificity were equal.
Table 2.
The results of subgroup analyses.
Fig 5.
The Deeks’ funnel plot and asymmetry test of the meta-analysis of the 36 included studies.