The Potential of Aqueous Humor Sampling in Diagnosis, Prognosis, and Treatment of Retinoblastoma
Bibliographic record
Abstract
Retinoblastoma (RB) is a rare malignant tumor that arises in the developing retina in one or both eyes of children. Pathogenic variants of the RB1 tumor suppressor gene drive the majority of germline and sporadic RB tumors. Considering the risk of tumor spread, the biopsy of RB tumor tissue is contraindicated. Advancement of chemotherapy has led to preservation of more eye globes. However, this has reduced access to tumor material from enucleation specimens. Recently, liquid biopsy of aqueous humor (AH) has advanced the RB tumor- or eye-specific genetic analysis. In particular, nucleic acid analysis of AH demonstrates the genomic copy number profiles and RB1 pathogenic variants akin to that of enucleated RB eye tissue. This advance reduces the previous limitation that genetic assessment of the primary tumor could be done only after enucleation of the eye. Additionally, nucleic acid evaluation of AH allows the exploration of the genomic landscape of RB tumors at diagnosis and during and after treatment. This review explores how AH sampling and AH nucleic acid analysis in RB patients assist in diagnosis, prognosis, and comprehending the pathophysiology of RB, which will ultimately benefit individualized treatment decisions to carefully manage this ocular cancer in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".