Histopathological diagnosis of tumour deposits in colorectal cancer: a Delphi consensus study
Bibliographic record
Abstract
AIMS: Tumour deposits (TDs) are an important prognostic marker in colorectal cancer. However, the classification, and inclusion in staging, of TDs has changed significantly in each tumour-node-metastasis (TNM) edition since their initial description in TNM-5, and terminology remains controversial. Expert consensus is needed to guide the future direction of precision staging. METHODS AND RESULTS: A modified Delphi consensus process was used. Statements were formulated and sent to participants as an online survey. Participants were asked to rate their agreement with each statement on a five-point Likert scale and also to suggest additional statements for discussion. These responses were circulated together with anonymised comments, and statements were modified prior to carrying out a second online round. Consensus was set at 70%. Overall, 32 statements reached consensus. There were concerns that TDs were currently incorrectly placed in the TNM system and that their prognostic importance was being underestimated. There were concerns regarding interobserver variation and it was felt that a clearer, more reproducible definition of TDs was needed. CONCLUSIONS: Our main recommendations are that the number of TDs should be recorded even if lymph node metastases (LNMs) are also present and that nodules with evidence of origin [extramural venous invasion (EMVI), perineural invasion (PNI), lymphatic invasion (LI)] should still be categorised as TDs and not excluded, as TNM-8 specifies. Whether TDs should continue to be included in the N category at all is controversial, and did not achieve consensus; however, participants agreed that TDs are prognostically worse than LNMs and the N1c category is suboptimal, as it does not reflect this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".