Minimally invasive cytoreduction for advanced-stage ovarian cancer—a narrative review
Bibliographic record
Abstract
Background and Objective: Ovarian cancer is associated with high morbidity and mortality. Traditional open surgical approaches for ovarian cancer often entail extensive radical surgery leading to prolonged recovery times. Minimally invasive surgery (MIS) has become a promising alternative for gynecological malignancies, offering potential benefits such as reduced perioperative morbidity, faster recovery, and comparable oncologic outcomes. Despite increasing adoption, the role of MIS in the management of ovarian cancer requires further elucidation. This narrative review aims to summarize the current evidence on the role of MIS in the treatment of advanced-stage ovarian cancer. Specifically, it seeks to evaluate the feasibility, and oncologic outcomes associated with MIS approaches, including laparoscopic and robotic-assisted techniques. Methods: A narrative review was performed, including all pertinent publication pertaining to MIS cytoreduction for advanced stage ovarian cancer. We did not follow a systematic review pathway and included all study designs. Key Content and Findings: MIS cytoreduction for advanced stage ovarian cancer is feasible and gaining popularity, mainly in the context of increased utilization of neoadjuvant chemotherapy. No level I evidence is available regarding the oncological safety of this approach, but notwithstanding a significant risk of selection bias, retrospective data suggest that the progression-free survival and overall survival might not be jeopardised. Conclusions: In an era where lifespan, but also health span are gaining importance, tailored cytoreductive surgery using MIS could be of value for selected patients, aiming to minimize morbidity and maximize quality of life in advanced stage ovarian cancer without compromising oncologic outcomes. Based on the available published literature, MIS cytoreduction, mainly since the introduction of neo-adjuvant chemotherapy and the robotic platform, is a promising option for patients and warrants further investigation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".