Impact of tissue sampling on detection of venous invasion in colorectal cancer: a prospective analysis
Bibliographic record
Abstract
AIMS: Venous invasion (VI) is a powerful yet under-reported prognostic factor in colorectal cancer (CRC). Efforts to improve its detection have largely focused upon histological assessment, with less attention paid to tissue-sampling strategies. This study aimed to prospectively determine the number of tumour blocks required to optimise VI detection in CRC resections. In addition, the relationship between linear spiculation (LS) and extramural venous invasion (EMVI) was investigated. METHODS AND RESULTS: A standardised tissue sampling protocol was developed and applied prospectively to 217 CRC resections [AJCC 8th edition, stage 1 (n = 32); stage 2 (n = 84); stage 3 (n = 87); stage 4 (n = 14); and post-neoadjuvant therapy (n = 46)]. Elastin stains were performed on all tumour blocks. VI was identified in 55% of cases (EMVI = 37%; IMVI alone = 18%). The sensitivity of VI detection increased with increasing numbers of tumour blocks submitted [one block (35%), three blocks (66%), five blocks (84%), six blocks (95%) and seven blocks (97%)]. Similar findings were observed for EMVI [one block (35%), three blocks (73%), five blocks (89%), six blocks (96%) and seven blocks (96%)]. LS was identified macroscopically in 22% of specimens. In cases where no neoadjuvant therapy had been given, EMVI was significantly associated with LS (71% in LS+ cases versus 29% in LS- cases; P < 0.001). In addition, tumour blocks targeting LS were associated with a fivefold higher rate of EMVI compared with blocks that did not (P < 0.001). CONCLUSIONS: Our findings demonstrate the impact of tissue sampling and quality of gross examination on VI detection and may inform practices in future CRC protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".