A New Prognostic Staging System for Rectal Cancer
Bibliographic record
Abstract
In Brief Objective: To clarify the appropriateness of tumor “budding,” a quantifiable histologic variable, as 1 parameter in the construction of a new prognostic grading system for rectal cancer. Summary Background Data: Patient division according to an accurate prognostic prediction could enhance the effectiveness of postoperative adjuvant therapy and follow-up. Patients and Methods: Tumor budding was defined as an isolated cancer cell or a cluster composed of fewer than 5 cells in the invasive frontal region, and was divided into 2 grades based on its number within a microscopic field of ×250. We analyzed 2 discrete cohorts comprising 638 and 476 patients undergoing potentially curative surgery. Results: In the first cohort, high-grade budding (10 or more foci in a field) was observed in 30% of patients and was significantly associated with a lower 5-year survival rate (41%) than low-grade budding (84%). Similarly, in the second cohort, the 5-year survival rate was 43% in high-grade budding patients and 83% in low-grade budding patients. In both cohorts, multivariate analyses verified budding to be an independent prognosticator, together with nodal involvement and extramural spread. These 3 variables were given weighted scores, and the score range was divided to provide 5 prognostic groups (97%; 86%; 61%; 39%; 17% 5-year survival). The model was tested on the second cohort, and similar prognostic results were obtained. Conclusions: We propose that because of its relevance to prognosis and its reproducibility, budding is an excellent parameter for use in a grading system to provide a confident prediction of clinical outcome. Based on 2 discrete cohorts of rectal cancer patients, tumor “budding” was identified as a reproducible independent prognostic indicator that can improve the erroneous staging of advanced disease as early-stage disease. A grading system using the 3 parameters (tumor depth, nodal involvement, and budding) provided a wider spectrum of 5-year survival rates (18–98%).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".