Liquid biopsy in hepatocellular carcinoma: circulating tumor cells and circulating tumor DNA
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers and a leading cause of death worldwide. Due to latent liver disease, late diagnosis, and nonresponse to systemic treatments, surgical resection and/or biopsy specimens are still generally considered as the gold standard by clinicians for clinical decision-making until now. Since the conventional tissue biopsy is invasive and contains small tissue samples, it is unable to represent tumor heterogeneity or monitor dynamic tumor progression. Therefore, it is imperative to find a new less invasive or noninvasive diagnostic strategy to detect HCC at an early stage and to monitor HCC recurrence. Over the past years, a new diagnostic concept known as "liquid biopsy" has emerged with substantial attention. Liquid biopsy is noninvasive and allows repeated analyses to monitor tumor recurrence, metastasis or treatment responses in real time. With the advanced development of new molecular techniques, HCC circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) detection have achieved interesting and encouraging results. In this review, we focus on the clinical applications of CTCs and ctDNA as key components of liquid biopsy in HCC patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".