Phase I study on sentinel lymph node mapping in colon cancer: A preliminary report*
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Lymph node (LN) metastasis is one of the most significant prognostic factor in colorectal cancer. In fact, therapeutic decisions are based on LN status. However, multiple studies have reported on the limitations of the conventional pathological LN examination techniques, and therefore, the actual number of patients with LN positive colorectal cancer is probably underestimated. We assume that lymphatic tumor dissemination follows an orderly sequential route. We report here a simple and harmless coloration technique that was recently elaborated, and that allows us to identify the sentinel LN(s) (SLN) or first relay LNs in colorectal cancer patients. The main endpoint of this clinical trial is the feasibility of the technique. METHODS: Twenty patients treated by surgery for a colic cancer were admitted in this protocol. A subserosal peritumoral injection of lymphazurin 1% was performed 10 min before completing the colic resection. A pathologist immediately examined the specimens, harvested the colored SLN, and examined them by serial cuts (200 microm) with H&E staining, followed by immunohistochemical staining (AE1-AE3 cytokeratin markers), when serial sections were classified as cancer free. RESULTS: The preoperative identification of the SLN was impossible in at least 50 of the cases, however, SLNs were identified by the pathologist in 90% of cases. In two patients (10%) SLN was never identified. The average number of SLN was 3.9. Immunohistochemical analysis of the SLN has potentially changed the initial staging (from Dukes B to Dukes C) for 5 of the 20 patients (25%). On the other hand, there was one patient (5%) with hepatic metastasis from adenocarcinoma for whom SLN pathology was negative for metastasis (skip metastasis). CONCLUSIONS: SLN biopsy is readily feasible with identification of SLN in at least 90% of patients with colorectal cancers. Our results indicate that 45% of patients initially staged as Dukes B had tumor cells identified in their SLN when these were subjected to our protocol. This represented a 25% upgrading rate when our complete study population is considered. However, controversy persist about the clinical significance and metastatic potential of these often very small clusters of tumor cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".