New method of indocyanine green fluorescence sentinel node mapping for early gastric cancer
Bibliographic record
Abstract
Background: The present study describes the retrospective feasibility study of ICG fluorescence SN mapping in back-table for early gastric cancer using PINPOINT®. Method: SN mapping were performed as following; the day before surgery, 0.5 ml ICG was injected endoscopically in four quadrants of the submucosa surrounding the gastric cancer using an endoscopic puncture. Intraoperatively, the gastrocolic ligament was divided to visualize all possible directions of lymphatic flow from the stomach. PINPOINT® (NOVADAQ, Canada) was used to illuminate regional lymph nodes from the serosal side. Positive staining was confirmed by at least 3 surgeons and an endoscopist during surgery (Figure 1). Lymph node dissection and gastrectomy were performed according to the criteria of gastric cancer treatment guidelines of JGCA. Result: All 6 patients had gastrectomy with laparoscopic approach. ICG positive lymphatic flow and lymph nodes were able to be observed in all the patients. Final pathological diagnosis was all StageI and curative resection. All the patients had ICG positive lymphatic area in left gastric artery (LGA) area. Two patients with tumor located in L area had ICG positive flow to right gastroepipoloic artery (RGEA) area. The mean of ICG positive lymph nodes was 8.6. One patient had a metastatic lymph node in station No.4, which was positive for ICG. Conclusion: Our method made identification of ICG positive lymph nodes easy in SN mapping in back-table under room light. Although further accumulation and analysis are necessary, we may be able to apply this method for intraoperative SN mapping of laparoscopic gastric cancer surgey. HIGHLIGHTS ICG fluorescence SN mapping for early gastric cancer using PINPOINT® is described. ICG positive nodes were able to be observed in all the patients. The mean of ICG positive lymph nodes was 8.6. One patient had a metastatic lymph node in SN. PINPOINT® make identification of SNs easy and simple for gastric cancer surgey.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".