Adjuvant Chemotherapy Improves Survival for Children With Massive Choroidal Invasion of Retinoblastoma
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the effect of adjuvant chemotherapy on outcomes of children with massive choroidal invasion (MCI). Methods: In this study, we reviewed the 5-year relapse-free survival (RFS) and overall survival (OS) of children diagnosed with MCI, managed with or without adjuvant chemotherapy. Excluded were children with additional other high-risk features (post-laminar optic nerve invasion, scleral invasion, or overt extraocular disease). Results: Of 3566 children diagnosed with retinoblastoma, 2023 had enucleation, and 60 eyes of 60 children had pathology showing MCI without concomitant high-risk features. Enucleation was primary (22, 37%), or secondary (38, 63%) after failed eye salvage. Adjuvant systemic chemotherapy (median = 4, range = 1-8 cycles) was given to 48 of 60 (80%) children; 12 of 60 (20%) children had no adjuvant therapy. Five-year RFS was 88.5% (95% confidence interval [CI] = 79.7%-97.3%) and 5-year OS was 90.1% (95% CI = 81.7%-98.5%). Pre-enucleation chemotherapy did not affect RFS (89.7% vs. 75.0%; P = 0.657). Adjuvant chemotherapy improved RFS (97.2% vs. 55.6%; P < 0.001) and OS (97.2% vs. 66.7%; P < 0.001). In subgroup analysis, adjuvant chemotherapy improved RFS for both primarily enucleated (5-year RFS 100% vs. 50.0%; P = 0.002) and secondarily enucleated children (5-year RFS 95.8% vs. 60.0%; P = 0.005). The number of children treated with adjuvant chemotherapy to prevent one post-enucleation systemic relapse or death is three. Conclusions: Adjuvant chemotherapy significantly decreased the risk of tumor relapse and death for children with pathological MCI. For every three children treated with adjuvant chemotherapy, one systemic relapse or death could be prevented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| 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 teacher head, 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".