Metronomic Chemotherapy: Possible Clinical Application in Advanced Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a hypervascular highly angiogenic tumor usually associated with liver cirrhosis. Vascular endothelial growth factor plays a critical role in vascular development in HCC. In contrast to the treatment of early-stage HCC, the treatment options for advanced HCC are limited and prognosis is often poor, which contributes to this tumor type being the third leading cause of cancer-related deaths worldwide. Metronomic chemotherapy, which was originally designed to inhibit angiogenesis, involves low-dose chemotherapeutic agents administered in a frequent regular schedule with no prolonged breaks and minimizes severe toxicities. We reviewed the potential effects and impact of metronomic chemotherapy in preclinical studies with HCC models and in patients with advanced HCC, especially when combined with a molecular targeted agent. Metronomic chemotherapy involves multiple mechanisms that include antiangiogenesis and antivasculogenesis, immune stimulation by reducing regulatory T cells and inducing dendritic cell maturation, and possibly some direct tumor cell targeting effects, including the cancer stem cell subpopulation. The total number of preclinical studies with HCC models shows impressive results using metronomic chemotherapy-based protocols, especially in conjunction with molecular targeted agents. Four clinical trials and two case reports evaluating metronomic chemotherapy for HCC indicate it to be a safe and potentially useful treatment for HCC. Several preclinical and clinical HCC studies suggest that metronomic chemotherapy may become an alternative type of chemotherapy for advanced unresectable HCC and postsurgical adjuvant treatment of HCC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".