Angiogenesis Signaling in Retinoblastoma: Prognostic and Therapeutic Applications
Bibliographic record
Abstract
Angiogenesis is a critical player in tumor metastasis that is involved in the pathophysiology of the pediatric ocular cancer retinoblastoma (RB). This review summarizes evidence linking angiogenesis to RB prognostication, response to treatment, and therapy. Vascular endothelial growth factor (VEGF), a major proangiogenic growth factor, has potential as a biomarker of therapy response in RB treatment. High VEGF correlates with poor chemotherapy response, subsequent local invasion, and lower patient survival. VEGF levels are also strongly correlated with choroidal invasion, poor differentiation, and an overall negative disease prognosis for RB patients. In contrast, decreasing VEGF levels can predict vitreous seed regression after intravitreal chemotherapy. Further investigation is needed to determine the accuracy and clinical value of using aqueous humor liquid biopsies to assess VEGF levels to predict prognosis or therapy response. Antiangiogenic agents, including approved drugs and experimental compounds, have shown potential in RB models and may become potential therapeutics, adjuvants to current chemotherapies, or treatments for chemotherapy complications, although there is limited evidence that antiangiogenic monotherapy may be sufficient for RB. Overall, future research aimed at integrating angiogenesis markers and therapies with existing RB strategies holds promise for improving patient outcomes and personalizing treatment approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".