Tumor Deposits in Colorectal Cancer: Definitions for Ninth Edition of the Tumor Node Metastasis Staging System
Bibliographic record
Abstract
Tumor deposits (TDs) have been a contentious element of the tumor node metastasis staging system for colorectal cancer since their introduction in 1997. Classified within the nodal category, their definition has changed repeatedly due to unclear distinctions from lymph node metastases, extramural vascular invasion, and perineural invasion. Despite updates in the tumor node metastasis system eight edition, ambiguity remains, with current criteria relying heavily on pathologist discretion. The fact that TDs are among the most powerful prognostic indicators warrants standardization, based on scientific evidence. A Delphi consensus among expert pathologists confirmed the lack of specificity and reproducibility in the current definition. In response, a new definition was developed, identifying TDs as discrete tumor nodules in pericolic or perirectal fat, distinct from lymph nodes, extramural vascular invasion, or perineural invasion but possibly originating from them. This definition emphasizes the need to report TDs separately when there is unequivocal tumor extension in relation to vessels or nerves. Size and distance from the primary tumor are debated as potential criteria, although they are not part of the proposed definition. The new definition is a first step to incorporate a more robust, biologically relevant definition of TDs into cancer staging.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".