Performance of Indocyanine Green Compared to 99mTc-Nanocolloids for Sentinel Lymph Node Detection in Early Vulvar Cancer
Bibliographic record
Abstract
STUDY OBJECTIVE: The aim of this study was to evaluate the performance of indocyanine green (ICG) compared to that of the gold standard 99mtechnetium (99mTc-nanocolloids) in detecting sentinel lymph nodes (SLN) in early vulvar cancer. MATERIAL AND METHODS: A single-center retrospective cohort study comparing SLN detection by 99mTc-nanocolloids and ICG was performed in patients presenting early vulvar cancer (T1/2), with clinically negative nodes. All SLN showing a radioactive and/or fluorescent signal were resected. The primary endpoints were the sensitivity, positive predictive value (PPV) and false negative (FN) rate of ICG in detecting SLN compared to 99mTc-nanocolloids. RESULTS: Thirty patients were included and 99 SLN were identified in 43 groins. Compared to 99mTc-nanocolloids, ICG had a sensitivity of 80.8% (95% CI [72.6; 88.6%]), a PPV of 96.2% (95% CI [91.8; 100%]) and a FN rate of 19.1% in detecting SLN. Seventeen (17.1%) infiltrated (positive) SLN were identified out of the 99 SLN detected. Compared to 99mTc-nanocolloids, ICG showed a sensitivity of 82.3% (95% CI [73.1; 91.5%]), a PPV of 100% and a FN rate of 17.6% (3/17) in detecting infiltrated SLN. CONCLUSION: Despite its many advantages, ICG cannot be used as the sole tracer for the detection of SLN in early vulvar cancer and should be employed in conjunction with 99mTc-nanocolloids.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".