Liquid biopsy in hepatobiliary and pancreatic cancers: A paradigm shift in early detection, prognostic stratification, and perioperative monitoring
Bibliographic record
Abstract
BACKGROUND Hepatobiliary and pancreatic cancers are among the most lethal malignancies due to late-stage diagnosis and limited treatment options. Liquid biopsy has emerged as a minimally invasive tool for early cancer detection, prognosis, and therapeutic monitoring. AIM To concise the available data on liquid biopsy and establish its role in hepatobiliary surgeries. METHODS This systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and EMBASE for studies published up to March 2025. Studies assessing the role of circulating tumor DNA, circulating tumor cells, exosomes, and other liquid biopsy markers in hepatobiliary and pancreatic cancers were included. The risk of bias was evaluated using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias Tool for clinical trials. RESULTS Liquid biopsy demonstrated significant potential for early cancer detection, perioperative risk stratification, intraoperative surgical decision-making, and postoperative monitoring of minimal residual disease. However, challenges remain regarding standardization, sensitivity, and clinical validation. CONCLUSION Liquid biopsy represents a paradigm shift in hepatobiliary and pancreatic cancer management. Advancements in next-generation sequencing and artificial intelligence may enhance its clinical utility. Further large-scale studies are needed to establish standardized protocols for routine implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".