Fig 1.
PRISMA flowchart illustrating the process of screening and selection of studies.
Table 1.
Baseline Demographic and Geographic Characteristics of Study Populations.
Table 2.
Study Characteristics and Diagnostic Outcomes.
Table 3.
Biomarker Types, Detection Methods, and Sample Types.
Fig 2.
Risk of Bias and Applicability Concerns Assessment Using QUADAS-2.
Fig 3.
Forest Plots, SROC Curve, and Diagnostic Performance Analyses of Protein Biomarkers for Early Pancreatic Cancer Detection.
A: Forest plots showing pooled sensitivity (left) and specificity (right) estimates of included studies with 95% confidence intervals (CIs). The red dashed lines indicate the overall pooled sensitivity and specificity. B: Forest plots of positive likelihood ratios (LR+) and negative likelihood ratios (LR-) with pooled estimates. LR+ measures the likelihood of a positive test result in patients with the disease, while LR- assesses the likelihood of a negative test in patients without the disease. C: Forest plots of diagnostic score (log diagnostic odds ratio) and diagnostic odds ratio (DOR) with corresponding 95% CIs. Higher DOR values indicate stronger overall diagnostic accuracy. D: Summary Receiver Operating Characteristic (SROC) curve with prediction and confidence contours, depicting the diagnostic accuracy of biomarkers across studies. The red point represents the summary operating point, and the dotted lines indicate the confidence and prediction intervals. E: Deeks’ funnel plot asymmetry test for publication bias assessment. A non-significant p-value (p > 0.05) suggests no evidence of significant publication bias. F: Fagan’s nomogram demonstrating post-test probabilities based on a pretest probability of 50%. Positive tests increase post-test probability to 87%, while negative tests reduce it to 19%. G: Scatter plot of positive likelihood ratio (PLR) versus negative likelihood ratio (NLR) across studies, illustrating variability in diagnostic accuracy.
Fig 4.
Forest Plots, SROC Curve, and Diagnostic Performance Analyses of ctDNA Biomarkers for Early Pancreatic Cancer Detection.
A: Forest plots showing pooled sensitivity (left) and specificity (right) estimates of included studies with 95% confidence intervals (CIs). The red dashed lines indicate the overall pooled sensitivity and specificity. B: Forest plots of positive likelihood ratios (LR+) and negative likelihood ratios (LR-) with pooled estimates. LR+ measures the likelihood of a positive test result in patients with the disease, while LR- assesses the likelihood of a negative test in patients without the disease. C: Forest plots of diagnostic score (log diagnostic odds ratio) and diagnostic odds ratio (DOR) with corresponding 95% CIs. Higher DOR values indicate stronger overall diagnostic accuracy. D: Summary Receiver Operating Characteristic (SROC) curve with prediction and confidence contours, depicting the diagnostic accuracy of biomarkers across studies. The red point represents the summary operating point, and the dotted lines indicate the confidence and prediction intervals. E: Deeks’ funnel plot asymmetry test for publication bias assessment. A non-significant p-value (p > 0.05) suggests no evidence of significant publication bias. F: Fagan’s nomogram showing post-test probabilities for a pretest probability of 50%. A positive test increases the post-test probability to 91%, while a negative test decreases it to 27%. G: Scatter plot of positive likelihood ratio (PLR) versus negative likelihood ratio (NLR) across studies, illustrating variability in diagnostic accuracy.
Fig 5.
Forest Plots, SROC Curve, and Diagnostic Performance Analyses of miRNA Biomarkers for Early Pancreatic Cancer Detection.
A: Forest plots showing pooled sensitivity (left) and specificity (right) estimates of included studies with 95% confidence intervals (CIs). The red dashed lines indicate the overall pooled sensitivity and specificity. B: Forest plots of positive likelihood ratios (LR+) and negative likelihood ratios (LR-) with pooled estimates. LR+ measures the likelihood of a positive test result in patients with the disease, while LR- assesses the likelihood of a negative test in patients without the disease. C: Forest plots of diagnostic score (log diagnostic odds ratio) and diagnostic odds ratio (DOR) with corresponding 95% CIs. Higher DOR values indicate stronger overall diagnostic accuracy. D: Summary Receiver Operating Characteristic (SROC) curve with prediction and confidence contours, depicting the diagnostic accuracy of biomarkers across studies. The red point represents the summary operating point, and the dotted lines indicate the confidence and prediction intervals. E: Deeks’ funnel plot asymmetry test for publication bias assessment. A non-significant p-value (p > 0.05) suggests no evidence of significant publication bias. F: Fagan’s nomogram demonstrating post-test probabilities based on a pretest probability of 50%. A positive test result increases the post-test probability to 91%, while a negative test reduces it to 12%. G: Scatter plot of positive likelihood ratio (PLR) versus negative likelihood ratio (NLR) across studies, illustrating variability in diagnostic accuracy.
Fig 6.
Forest Plots, SROC Curve, and Diagnostic Performance Analyses of Metabolite Biomarkers for Early Pancreatic Cancer Detection.
A: Forest plots showing pooled sensitivity (left) and specificity (right) estimates of included studies with 95% confidence intervals (CIs). The red dashed lines indicate the overall pooled sensitivity and specificity. B: Forest plots of positive likelihood ratios (LR+) and negative likelihood ratios (LR-) with pooled estimates. LR+ measures the likelihood of a positive test result in patients with the disease, while LR- assesses the likelihood of a negative test in patients without the disease. C: Forest plots of diagnostic score (log diagnostic odds ratio) and diagnostic odds ratio (DOR) with corresponding 95% CIs. Higher DOR values indicate stronger overall diagnostic accuracy. D: Summary Receiver Operating Characteristic (SROC) curve with prediction and confidence contours, depicting the diagnostic accuracy of biomarkers across studies. The red point represents the summary operating point, and the dotted lines indicate the confidence and prediction intervals. E: Deeks’ funnel plot asymmetry test for publication bias assessment. A non-significant p-value (p > 0.05) suggests no evidence of significant publication bias. F: Fagan’s nomogram demonstrating post-test probabilities. Based on a pretest probability of 50%, a positive test result increases the post-test probability to 85%, while a negative test reduces it to 15%. G: Scatter plot of positive likelihood ratio (PLR) versus negative likelihood ratio (NLR) across studies, illustrating variability in diagnostic accuracy.
Fig 7.
Combined Diagnostic Performance of CA19-9 with Protein Biomarkers and ctDNA for Early Pancreatic Cancer Detection.
Fig 8.
Diagnostic Performance of Biomarkers in Healthy Control Populations for Early Pancreatic Cancer Detection.
Fig 9.
Diagnostic Accuracy of Protein Biomarkers and miRNAs Using Specific Detection Methods for Early Pancreatic Cancer.