Circulating tumor DNA as a minimal residual disease assessment and recurrence risk in hepatocellular carcinoma: A systematic review and meta-analysis.
Bibliographic record
Abstract
e15055 Background: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the mainstay of curative treatment for patients (pts) with HCC confined to the liver remains hepatectomy, thermal ablation, or liver transplantation. However, despite these approaches, cancer relapses are still high and strongly correlated with the presence of residual disease following curative-intent treatment. Hence, endeavours to refine risk stratification and identify pts more likely to recur is critical. To this end, circulating tumour DNA (ctDNA) is promising. This meta-analysis seeks to assess the prognostic role of plasma ctDNA in pts diagnosed with HCC undergoing curative treatment. Methods: A systematic search of MEDLINE, EMBASE, and Cochrane databases up to November 2024 was carried out to identify studies investigating plasma ctDNA collection in pts with early or intermediate- stage Barcelona Clinic Liver Cancer (BCLC) HCC undergoing curative-intent treatment, baseline (before radical treatment) and landmark time points (after radical treatment). The hazards ratios (HRs) with 95% confidence intervals (CIs) were pooled for recurrence- free survival (RFS) and overall survival (OS) using a random-effects model. Results: A total of 10 retrospective studies, encompassing 928 pts with plasma samples available at baseline and landmark timepoints, were included. Plasma samples were obtained up to 12 weeks postoperatively. Six studies utilized a tumor-informed approach for ctDNA analysis, 2 employed a tumor-agnostic approach, and 1 incorporated a combined analysis of both methods. ctDNA detection varied, with 6 studies using next-generation sequencing (NGS) and 3 using NGS combined with droplet digital polymerase chain reaction (ddPCR). Pts with detectable postoperative ctDNA had significantly shorter RFS compared to those with undetectable ctDNA levels (HR: 4.48, 95% CI [2.46, 8.16]; I² = 80%, p < 0.001). Baseline ctDNA detection was significantly associated with shorter RFS (HR 4.71, 95% CI [2.35–9.41]; I² = 0%, p < 0.001). Likewise, ctDNA positivity at the landmark time point correlated with reduced OS (HR 2.99, 95% CI [1.94–4.61]; I² = 47%, p < 0.001). Four studies with available data were analyzed, with sensitivities ranging from 33% to 82% and specificities ranging from 41% to 100%, highlighting variability in diagnostic performance across studies. Leave-one-out analysis confirms ctDNA's prognostic value for higher RFS (HR 4.48, 95% CI [2.48–8.16]) without any study disproportionately influencing results. Conclusions: The detection of ctDNA following curative-intent treatment in pts with early-stage HCC was prognostic. There is potential to leverage plasma ctDNA in prospective studies, which may improve risk stratification and aid treatment selection.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".