Practical guidance for assessing and reporting lymphovascular space invasion (LVSI) in endometrial carcinoma
Bibliographic record
Abstract
Lymphovascular space invasion (LVSI) is an important prognostic parameter in endometrial carcinoma (EC) and has gained increasing interest in recent years due to an expanding body of evidence of its independent prognostic value, especially when the presence of LVSI is quantified. A key strength of LVSI as a prognostic factor is that it can be detected on routine microscopic examination, without ancillary tests, and thus can be used in low-resource settings. A weakness, however, is the lack of uniformly applied criteria for assessment and quantification of LVSI, resulting in interobserver variation in diagnosis. This is confounded by artefacts and other morphological features that may mimic LVSI (commonly referred to as pseudo-LVSI). Despite these issues, multiple studies have shown that LVSI is strongly associated with lymph node (LN) metastasis and is an independent risk factor for LN recurrence and distant metastasis. Consequently, the presence of substantial/extensive LVSI has become an important consideration in formulating adjuvant treatment recommendations in patients with EC, and this has been incorporated in the recent International Federation of Gynecology and Obstetrics (FIGO) 2023 staging system. Herein, we review the current literature on LVSI in EC and discuss its role as a prognostic marker, the reproducibility of LVSI assessment and distinction between LVSI and its mimics. We provide illustrations of key diagnostic features and discuss the two-tiered (none/focal versus substantial) system of LVSI classification. This work is intended to provide guidance to practising pathologists and unify the approach towards LVSI assessment in EC.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".