LEOPARD trial – Lymphadenectomy in Endometrioid Ovarian carcinoma PAtients with eaRly stage Disease
Bibliographic record
Abstract
Introduction: According to the WHO2020 understanding endometrioid ovarian carcinoma (ENOC) is the second most frequent ovarian carcinoma histotype. The benefit of lymph node surgery (LNS) in early-stage ENOC is unknown. Prior studies examining the benefit of LNS in ENOC have been hampered by small numbers, and large-scale studies that consider modern classification are needed. Methods: A cohort of 943 ENOC was assembled from 22 centers across Canada and Europe. Histotype was confirmed by central expert pathology review and immunohistochemistry, followed by extensive chart review. Complete lymphadenectomy was defined as such when at least 10 pelvic and 10 paraaortic LN were removed. Results: Chart review and histopathologic data was available from 721 patients. Median age at diagnosis was 55.57 years (28-94). 438(60.7%) patients were diagnosed with FIGO stage I, 170(23.6%) with stage II, 92(12.8%) with stage III and 21(2.9%) with stage IV disease. Grade distribution included 325(45.1%) G1, 259(35.9%) G2 and 126(17.5%) G3 tumors. Surgical lymph node sampling was performed in 303(42.0%), complete lymphadenectomy in 105(14.6%) cases, revealing positive nodes in 30/408(7.3%) cases. All low-grade(G1) early-stage(pT1/2) cases were found to be node negative. Conclusion: The large international LEOPARD team initiative stands to provide a solid picture of this unique histotype including a powerful statement on the value of lymph-node dissection. We were able to show that lymph node involvement is rare, especially in early-stage ENOC. This type-specific approach will help to improve precision care for ENOC patients. Publication History Article published online: 01 October 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".