Use of an elastic stain to show venous invasion in colorectal carcinoma: a simple technique for detection of an important prognostic factor
Bibliographic record
Abstract
BACKGROUND: Venous invasion (VI) is an important prognostic factor in colorectal cancer; it is positively associated with visceral metastases and may affect the decision to treat with adjuvant therapy. AIMS: To evaluate whether an elastic tissue (Movat) stain facilitates identification of VI, the number of Movat-stained blocks needed to detect VI, and whether VI identified with a Movat stain is prognostically equivalent to VI identified on H&E-stained slides. METHODS: H&E-stained sections from colorectal carcinomas from the year 2000 (n = 92) were examined for VI and compared to Movat-stained slides. Clinical charts were reviewed to compare rates of metastases in VI-positive versus VI-negative patients. RESULTS: With the Movat stain, VI was identified in 44% of cases previously categorised as negative (p<0.001) on review of H&E slides alone. One Movat-stained section was often sufficient to identify VI, with a statistically significant benefit to performing multiple stains if necessary. In H&E sections, two clues helped identify VI: the "unaccompanied artery" sign, where large arteries were seen without an accompanying vein; and the "protruding tongue" sign, where smooth tongues of tumour extended into pericolic/rectal fat. Metastases were present in 61% of cases positive for VI compared to 35% in VI-negative cases (p = 0.03). 45% of cases positive for intramural VI only developed metastases (p = 0.39), while 65% of cases positive for extramural VI only developed metastases (p = 0.03). CONCLUSIONS: Pathologists should look for morphological clues of VI in H&E stained sections; when VI is not apparent, an elastic tissue stain on all tumour blocks significantly improves identification of VI. Morphological clues include the "unaccompanied artery" and "protruding tongue" signs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".