Bevacizumab Treatment for Low-Grade Serous Ovarian Cancer: A Systematic Review
Bibliographic record
Abstract
Serous epithelial ovarian cancer, classified as either high-grade (90%) or low-grade (10%), varies in molecular, histological, and clinicopathological presentation. Low-grade serous ovarian cancer (LGSOC) is a rare histologic subtype that lacks disease-specific evidence-based treatment regimens. However, LGSOC is relatively chemo-resistant and has a poor response to traditional treatments. Alternative treatments, including biologic therapies such as bevacizumab, have shown some activity in LGSOC. Thus, the objective of this systematic review is to determine the effect and safety of bevacizumab in the treatment of LGSOC. Following PRISMA guidelines, Medline ALL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase all from the OvidSP platform, ClinicalTrials.gov, International Clinical Trials Registry Platform, International Standard Randomised Controlled Trial Number Registry were searched from inception to February 2022. Articles describing bevacizumab use in patients with LGSOC were included. Article screening, data extraction, and critical appraisal of included studies were completed by two independent reviewers. The effect of bevacizumab on the overall response rate, progression-free survival, overall survival, and adverse effects were summarized. The literature search identified 3064 articles, 6 of which were included in this study. A total of 153 patients were analyzed; the majority had stage IIIC cancer (56.2%). The overall median response rate reported in the studies was 47.5%. Overall, bevacizumab is a promising treatment for LGSOC, with response rates higher than traditional treatment modalities such as conventional chemotherapy, and is often overlooked as a treatment tool. A prospective clinical trial evaluating the use of bevacizumab in LGSOC is necessary to provide greater evidence and support these findings.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".