Sentinel lymph node mapping in endometrial cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Appropriate extent of lymphadenectomy in clinically, early stage endometrial cancer remains controversial but sentinel lymph node (SLN) mapping has emerged as an alternative staging strategy, until the advent of molecular prognostic markers. We sought to perform a systematic review of the literature to determine pooled estimates for SLN detection rate and diagnostic accuracy, while exploring impact of the SLN on adjuvant therapy and oncologic outcomes. EVIDENCE ACQUISITION: We performed a systematic search utilizing Medline, EMBASE, and Web of Science electronic databases for all studies published in the English language until October 31, 2017. Studies were included for review and potential aggregate analyses if they contained at least 30 endometrial cancer patients with undergoing SLN mapping and reported on detection rates (overall, bilateral or para-aortic) or diagnostic accuracy (sensitivity and negative predictive value [NPV]). Pooled estimates were calculated via meta-analyses utilizing a random-effects model. Studies reporting on the impact of SLN on adjuvant therapy, as well as studies comparing SLN mapping to completion lymphadenectomy were qualitatively reviewed and analyzed as well. EVIDENCE SYNTHESIS: We identified 48 eligible studies, which included 5348 patients for review and inclusion in the meta-analysis for SLN detection or diagnostic accuracy. The pooled SLN detection rates were were 87% (95% CI: 84-89%, 44 studies) for overall detection, 61% (95% CI: 56-66%, 36 studies) for bilateral detection, and 6% (95% CI: 3-9%, 31 studies) for para-aortic detection. Indocyanine green use improved overall (94%, 95% CI: 92-96%, 19 studies) SLN detection rates compared to blue tracer (86%, 95% CI: 83-89%, 31 studies) or technetium-99 (86%, 95% CI: 83-89%, 25 studies). This trend was similarly seen in terms of bilateral detection rates (74% vs. 59% vs. 57%, respectively). There was no difference in para-aortic SLN detection rate between each tracer. The pooled estimates for diagnostic accuracy for 34 studies were 94% (95% CI: 91-96%) for sensitivity and 100% (95% CI: 99 - 100%) for NPV. Diagnostic accuracy of SLN mapping was not negatively affected in patients with high-grade endometrial histology. Patients with SLN mapping are more likely to receive adjuvant therapy and do not have inferior survival or recurrence outcomes compared to those undergoing completion lymphadenectomy. CONCLUSIONS: SLN mapping is a feasible and accurate alternative to stage patients with endometrial cancer. Utilizing indocyanine green results in the highest SLN detection rates. Future studies should prospectively examine the impact of SLN mapping on progression-free and overall survival.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".