Abstract A68: Optical coherence tomography improves subclinical retinoblastoma management
Bibliographic record
Abstract
Abstract Purpose: Optical coherence tomography (OCT) improved retinal assessment that is comparable to histopathologic sections. In retinoblastoma, OCT was reported to detect fovea-tumor relation, diagnose subclinical (new, residual and recurrent tumors), and suspect tumor spread into the optic nerve. We hypothesized that strategic incorporation of OCT into retinoblastoma management will improve outcomes and managing retinoblastoma at the subclinical level (clinically undetectable) provides means for less treatment burden. Methods: A single-center, noncomparative retrospective review of all eyes with subclinical retinoblastoma was conducted. Any eye that had an OCT-guided procedure, whether diagnostic or therapeutic, utilizing OCT software was included. OCT-guided management included projecting embedded OCT software tools (scan cube size and direction, multiple scan frames, localizing and measuring vertical and horizontal calipers, enhanced depth imaging frames, and/or red free fundus photograph) on a separate colored fundus image to guide management to improve visual, life, and eye salvage outcomes. Results: We used OCT-guided strategies in 83 children (114 eyes). Diagnostic strategies included posterior pole screening for invisible retinoblastoma (37 eyes), topographic localization of invisible tumors (15 eyes) or subclinical recurrence (29 tumors), topographic localization of fovea-tumor relation (31 eyes), and mapping of potential choroidal or optic nerve invasion to determine subsequent management (2 eyes each). Therapeutic strategies included laser photocoagulation for perifoveal residual (26) and invisible (11) tumors and verification of photocoagulation adequacy for invisible (11) new and recurrent (11) tumors. These procedures were scored for their influence to changed therapy. Conclusion: Detailed OCT assessment improves retinoblastoma management and facilitates precision therapy to preserve vital visual structures (e.g., the center of vision), detect subtle subclinical tumors (new or recurrent), allow administration and adequacy of laser treatment, and map subclinical high-risk features. Developing OCT-guided strategies with full technical description facilitates reproducibility and allows precision therapy to be a reality. Citation Format: Sameh E. Soliman, Cynthia VandenHooven, Leslie MacKeen, Brenda Gallie. Optical coherence tomography improves subclinical retinoblastoma management [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A68.
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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 | 0.003 |
| 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) | 0.002 | 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".