Neuronavigation-assisted pituitary neuroendocrine tumor resection: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The advancement of pituitary surgery has rendered it a secure and efficient treatment method; nevertheless, the potential for incomplete tumor removal and cerebrospinal fluid (CSF) leak remains. Neuronavigation-assisted pituitary neuroendocrine tumor (PitNET) resections have been driving a rising number of attentions in recent years. However, there is currently a lack of comprehensive quantitative evaluation of the effectiveness of neuronavigation-assisted pituitary tumor resection. We aimed to assess the curative effects and complications with or without the use of an image-based neuronavigation in PitNET resection. Methods: A systematic review and meta-analysis was performed by searching PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus from inception until May 1, 2024 in English to identify any studies reporting gross total resection (GTR) or postoperative complications in patients who underwent neuronavigation-assisted PitNET resection, excluding conference abstracts and studies with fewer than five subjects. We also searched the reference lists of previous systematic reviews and other relevant publications in databases. We reviewed and analyzed the studies that investigated the operative effects and complications of neuronavigation in PitNET resection. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was evaluated by funnel plot. Review manager 5.3 was employed for meta-analysis. The results were expressed as odds ratio (OR) with 95% confidence interval (CI) of image-assisted techniques for the incidence of GTR and complications. Results: A total of 42 publications that fulfilled the established searching criteria were obtained from the above-mentioned databases, all of which with the Newcastle-Ottawa Scale scores ≥ six ★. Among the included publications, 37 studies indicated that the OR of image-based neuronavigation was 2.29 (95% CI: 2.02–2.60, P<0.00001, I2=24%) for GTR. The other five studies compared the neuronavigation group (experimental group) and non-neuronavigation group (control group), exhibiting high heterogeneity (I2=91%). After sensitivity analysis, the results showed that the rate of the CSF leak of the neuronavigation group was slightly lower than that of the non-neuronavigation group (OR: 0.84, 95% CI: 0.73–0.97, P=0.01, I2=43%). Conclusions: According to the existing data, neuronavigation-assisted PitNET resection can increase the rates of GTR and reduce the incidence of postoperative complications. Our results provide a reference for the selection of surgical methods for PitNET resection in future clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".