Digital Assessment Of Depth Of Invasion In Melanoma Using Different Immunohistochemical Stains
Bibliographic record
Abstract
Introduction. The most important histologic prognostic factors for melanoma remain depth of invasion and mitotic rate. In a digital era, precision (in terms of reproducibility and inter laboratories repeatability), as well as image evidence, are new qualitative parameters. The objective of the study was to evaluate in a retrospective analysis any significant difference among the Breslow digital assessments performed using two different immunohistochemical stains and the gold standard, hematoxylin eosin stain. Material and methods. We evaluated 130 primary cutaneous melanomas diagnosed in a two-year period (2016-2017) with known Breslow scale. Immunohistochemistry staining (SOX10 and Melan A) was perform followed by slide scanning. Aperio Imagescope and StataCorp software were used for data acquisition and statics. Results. No significantly statistic differences were recorded between groups in terms of Breslow values (p=0.98, Kruskal-Wallis test). Conclusions. All three stains perform similar in evaluating depth of invasion regardless of the method used to quantify it. The pathologist should consider using an accurate and precise method like a digital measurement technique for daily reports.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".