Diagnosing Pathological Prognostic Factors in Retinoblastoma: Correlation between Traditional Microscopy and Digital Slides
Bibliographic record
Abstract
OBJECTIVE: It was the aim of this study to determine the diagnostic accuracy of high-risk prognostic factors and morphological characteristics of retinoblastomas using digital whole slide images (WSI) generated by a scanner. METHODS: Forty-seven H&E sections of glass slides with high-risk morphological features of retinoblastoma were analyzed. Slides were scanned as WSI and reviewed. The results were compared with those obtained after reviewing the slides using a regular microscope as the gold standard. McNemar's test (MT), the percentage of agreement (POA), and sensitivity (S) and specificity (Sp) were evaluated between WSI and conventional microscopy. RESULTS: There were no differences with respect to multicentricity, growth type, rosette formation, choroidal invasion, anterior chamber invasion, extraocular extension, scleral extension, optic nerve invasion, necrosis, or Azzopardi effect between WSI analysis and light microscopy (MT, p = 1.0; POA = 100%; S = 100%, and Sp = 100%). Discordance was found in 1 case where calcification could not be found using WSI (MT, p = 1.00; POA = 97.9%; S = 100%, and Sp = 97.8%). CONCLUSION: To the best of our knowledge, this is the first report using digital pathology (WSI) to evaluate prognostic factors in eyes containing retinoblastomas. Using WSI, the pathologist was able to detect high-risk morphological features in retinoblastoma. To date, WSI is an important tool, in particular for ophthalmic pathologists examining enucleation and exenteration specimens.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".