MRI Detection of Extramural Venous Invasion in Rectal Cancer: Correlation With Histopathology Using Elastin Stain
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to evaluate the diagnostic performance of MRI for detection of extramural venous invasion (EMVI) compared with histopathologic analysis using elastin stain. MATERIALS AND METHODS: Forty-nine patients with rectal cancer who had undergone surgical resection with preoperative MRI were identified. Thirty-seven patients had received preoperative chemoradiation therapy (CRT). Sixty-nine MRI studies were independently reviewed by two blinded radiologists for EMVI using a score of 0-4. Comparison was made with histopathologic results obtained by two pathologists reviewing the elastin-stained slides in consensus. EMVI status was also correlated with other tumoral and prognostic features on imaging and pathologic analysis. Statistical analysis was performed using Fisher exact and McNemar tests. RESULTS: EMVI was present in 31% of the pathology specimens. An MRI EMVI score of 3-4 was 54% sensitive and 96% specific in detecting EMVI in veins 3 mm in diameter or larger. Inclusion of a score of 2 as positive for EMVI increased the sensitivity to 79% but decreased the specificity to 74%, with poor positive predictive value. Preoperative CRT had no significant effect on the diagnostic performance of MRI. Contrast-enhanced MRI increased reader confidence for diagnosis or exclusion of EMVI compared with T2-weighted imaging. EMVI status correlated with depth of extramural invasion and proximity to mesorectal fascia. CONCLUSION: Despite an anticipated increase in sensitivity for EMVI detection by histopathologic analysis using elastin compared with H and E staining, MRI maintains a high specificity and moderate sensitivity for the detection of EMVI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".