Borderline Ovarian Tumors And Fertility-Preserving Surgery - A Systematic Review
Bibliographic record
Abstract
OBJECTIVES This systematic review evaluates fertility-preserving surgery (FPS) outcomes in borderline ovarian tumours (BOTs), focusing on reproductive success and oncologic safety. METHODOLOGY We systematically searched PubMed, MEDLINE, ScienceDirect, Google Scholar, the Cochrane Library, and ResearchGate (through September 14, 2024) using the terms "borderline ovarian tumour," "fertility-preserving surgery," and "reproductive outcome." From 2,288 initial records, we identified 10 high-quality (Newcastle-Ottawa Scale score ≥7) retrospective cohort studies (January 2019- September 2024) that met our eligibility criteria. The included English-language studies evaluated reproductive-aged women (14-49 years) with borderline ovarian tumours undergoing fertility-sparing surgery (cystectomy/USO). After excluding case reports, reviews, non-peer-reviewed articles, and duplicate publications, two reviewers independently extracted data, resolving discrepancies through consensus. We conducted this systematic review in accordance with the PRISMA guidelines, with registration on Prospero (Id: Crd420251042984). RESULTSAmong 1051 patients, pooled pregnancy rates ranged from 42.1% to 57.1%. Live birth rates varied widely (23-67%). Recurrence rates differed significantly by surgical approach: 24.1–33.3% after cystectomy versus 2.5-7.7% after USO. High-risk subgroups (advanced-stage/micropapillary histology) had recurrence rates up to 70.8%. Complete surgical staging reduced relapse risk by 21%, and ART did not increase recurrence. Bilateral cystectomy and USO + contralateral cystectomy showed comparable fertility outcomes. Malignant transformation was rare (0–20%), with no impact on overall 5-year survival (97–100%). CONCLUSION FPS provides reasonable reproductive outcomes but requires careful patient selection due to higher cystectomy-associated recurrence. Complete staging and histologic assessment are crucial. Until stronger evidence exists, USO with complete staging represents the most balanced option. Study limitations include retrospective designs and heterogeneous follow-up. Prospective trials with standardised protocols and long-term monitoring (at least 10 years) are needed.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".