Fluorescence-guided mesorectal nodes harvesting associated with local excision for early rectal cancer: technical notes
Bibliographic record
Abstract
BACKGROUND: The spread of colorectal cancer screening has increased the percentage of patients with early-stage rectal cancer; at least 30% of patients are diagnosed with a clinical-stage cT1 or pT1 after endoscopic excision. In this subgroup of patients, the real advantage of total mesorectal excision (TME) over local excision (LE) is the ability to remove mesorectal nodes, which are metastatic in less than 20% of cases. METHOD: To solve the unmet need for accurate nodal staging in patients with cT0/cT1, cN0 rectal cancer, we designed a pilot study that associates LE with mesorectal fluorescence-guided nodal sampling. From November 2018 to November 2023, we enrolled a total of ten patients with T1N0M0 rectal cancer. After extensive staging and adequate information, patients underwent endoscopic indocyanine green (ICG) infiltration and transanal local excision associated with laparoscopic fluorescence-guided mesorectal nodal sampling. RESULTS: After a median follow-up of 24 months (range 1-63 months), no case of local or nodal recurrence was observed. All patients were spared from ostomy and lower anterior resection syndrome. CONCLUSIONS: In selected cases of cT0-1cN0 rectal cancer, transanal local excision plus ICG lymph nodal sampling is a feasible surgical option that increases the rate of organ preservation. Further studies are needed to identify the patients most likely to benefit from this minimally invasive strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".