Validation of tissue microarrays in oral epithelial dysplasia using a novel virtual-array technique
Bibliographic record
Abstract
BACKGROUND: Malignant transformation risk in oral epithelial dysplasia (OED) is currently determined by histological assessment. The subjectivity of this approach has led to interest in identifying prognostic biomarkers. Tissue microarrays (TMA) can reduce the utilization of the finite resources of a pathological archive. However, the selectivity involved in TMA construction necessitates the need to ensure that individual cores are representative of the overall features of the donor specimen. We aimed to validate, for the first time, the use of the TMA technique in OED by using a novel virtual array technique. METHODS: Sections from 38 cases of OED were stained with H&E and 6 immunohistochemical (IHC) biomarkers. All were then digitally scanned. Virtual cores were generated by image capturing a 0.6mm(2) area of the IHC slide that corresponded to the same dysplastic area marked on the H&E slide. Two trained blinded observers scored both whole slides and virtual cores independently. The degree of reliability in scores between the individual raters and between virtual TMA cores and slides was assessed using both interclass correlation coefficient (ICCC) and weighted κ statistics. RESULTS: Excellent inter-observer reliability was demonstrated with all the immunohistochemical markers. ICCC ranged from 0.67-1.0 and κ scores >0.8. There was also a high reliability in the scores between whole slides and virtual TMAs, with ICCC of between 0.66 and 0.89 for the 6 markers. CONCLUSIONS: This study validates the use of TMAs in OED using a variety of biomarkers. We also report a novel method for achieving this using a novel virtual-array technique.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".