Sequential Circulating Tumor Cell Counts in Patients with Locally Advanced or Metastatic Hepatocellular Carcinoma: Monitoring the Treatment Response
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is among the most common causes of cancer death in men. Whether or not a longitudinal follow-up of circulating tumor cells (CTCs) before and at different time points during systemic/targeted therapy is useful for monitoring the treatment response of patients with locally advanced or metastatic HCC has been evaluated in this study. Blood samples (n = 104) were obtained from patients with locally advanced or metastatic HCC (n = 30) for the enrichment of CTCs by a negative selection method. Analysis of the blood samples from patients with defined disease status (n = 81) revealed that those with progressive disease (PD, n = 37) had significantly higher CTC counts compared to those with a partial response (PR) or stable disease (SD; n = 44 for PR + SD, p = 0.0002). The median CTC count for patients with PD and for patients with PR and SD was 50 (interquartile range 21–139) and 15 (interquartile range 4–41) cells/mL of blood, respectively. A longitudinal analysis of patients (n = 17) after a series of blood collections demonstrated that a change in the CTC count correlated with the patient treatment response in most of the cases and was particularly useful for monitoring patients without elevated serum alpha-fetoprotein (AFP) levels. Sequential CTC enumeration during treatment can supplement standard medical tests and benefit the management of patients with locally advanced or metastatic HCC, in particular for the AFP-low cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".