Feasibility and Performance of Elastin Trichrome as a Primary Stain in Colorectal Cancer Resection Specimens
Bibliographic record
Abstract
Venous invasion (VI) is a powerful prognostic factor in colorectal cancer (CRC) that is widely underreported. The ability of elastin stains to improve VI detection is now recognized in several international CRC pathology protocols. However, concerns related to the cost and time required to perform and evaluate these stains in addition to routine hematoxylin and eosin (H&E) stains remains a barrier to their wider use. We therefore sought to determine whether an elastin trichrome (ET) stain could be used as a "stand-alone" stain in CRC resections, by comparing the sensitivity, accuracy, and reproducibility of detection of CAP-mandated prognostic factors using ET and H&E stains. Representative H&E- and ET-stained slides from 50 CRC resections, including a representative mix of stages and prognostic factors, were used to generate 2 study sets. Each case was represented by H&E slides in 1 study set and by corresponding ET slides from the same blocks in the other study set. Ten observers (3 academic gastrointestinal [GI] pathologists, 4 community pathologists, 3 fellows) evaluated each study set for CAP-mandated prognostic factors. ET outperformed H&E in the assessment of VI with respect to detection rates (50% vs. 28.6%; P<0.0001), accuracy (82% vs. 59%, P<0.0001), and reproducibility (k=0.554 vs. 0.394). No significant differences between ET and H&E were observed for other features evaluated. In a poststudy survey, most observers considered the ease and speed of assessment at least equivalent for ET and H&E for most prognostic factors, and felt that ET would be feasible as a stand-alone stain in practice. If validated by others, our findings support the use of ET, rather than H&E, as the primary stain for the evaluation of CRC resections.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".