Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: The diagnostic and prognostic clinical value of circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) in pancreatic malignancies are unclear. Herein, we aimed to perform a meta-analysis to evaluate ctDNA and cfDNA as potential diagnostic and prognostic biomarkers.

Methods: PRISMA reporting guidelines were followed closely for conducting the current meta-analysis. The PubMed/Medline, Scopus, and Web of Science (WoS) databases were scanned in detail to identify eligible papers for the study. A quality assessment was performed in accordance with the REMARK criteria. The risk ratios (RRs) of the diagnostic accuracy of ctDNA compared to that of carbohydrate antigen 19.9 (CA 19.9) in all disease stages and the hazard ratios (HRs) of the prognostic role of ctDNA in overall survival (OS) were calculated with 95% confidence intervals (CIs).

Results: A total of 18 papers were evaluated to assess the diagnostic accuracy and prognostic value of biomarkers related to pancreatic malignancies. The pooled analysis indicated that CA19.9 provides greater diagnostic accuracy across all disease stages than ctDNA or cfDNA (RR = 0.64, 95% CI: 0.50-0.82, < 0.001). Additionally, in a secondary analysis focusing on prognosis, patients who were ctDNA-positive were found to have significantly worse OS (HR = 2.00, 95% CI: 1.51-2.66, < 0.001).

Conclusion: The findings of this meta-analysis demonstrated that CA19-9 still has greater diagnostic accuracy across all disease stages than KRAS mutations in ctDNA or cfDNA. Nonetheless, the presence of detectable levels of ctDNA was associated with worse patient outcomes regarding OS. There is a growing need for further research on this topic.

Systematic Review Registration: https://doi.org/10.37766/inplasy2023.12.0092, identifier INPLASY2023120092.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11231086PMC
http://dx.doi.org/10.3389/fonc.2024.1382369DOI Listing

Publication Analysis

Top Keywords

diagnostic accuracy
16
diagnostic prognostic
12
pancreatic malignancies
12
ctdna cfdna
12
disease stages
12
prognostic clinical
8
circulating tumor
8
tumor dna
8
cell-free dna
8
greater diagnostic
8

Similar Publications

RF phase modulation improves quantitative transient state sequences under constrained conditions.

MAGMA

September 2025

Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3585CX, Utrecht, The Netherlands.

Objective: Within gradient-spoiled transient-state MR sequences like Magnetic Resonance Fingerprinting or Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), it is examined whether an optimized RF phase modulation can help to improve the precision of the resulting relaxometry maps.

Methods: Using a Cramer-Rao based method called BLAKJac, optimized sequences of RF pulses have been generated for two scenarios (amplitude-only modulation and amplitude + phase modulation) and for several conditions. These sequences have been tested on a phantom, a healthy human brain and a healthy human leg, to reconstruct parametric maps ( and ) as well as their standard deviations.

View Article and Find Full Text PDF

Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.

View Article and Find Full Text PDF

Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.

Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.

View Article and Find Full Text PDF

Introduction: Breathlessness is a common cause of hospital admission globally and is associated with high mortality, particularly in low-income countries. In sub-Saharan Africa, there is a paucity of data on breathlessness, with existing data focused on individual diseases. There is a need for patient-centred approaches to understand interactions between multiple conditions to address population needs and inform health system responses.

View Article and Find Full Text PDF