Evidence map›Paper›PMID 39022256›Full record

ArticleQuantitative imaging in medicine and surgery2024

Neuronavigation-assisted pituitary neuroendocrine tumor resection: a systematic review and meta-analysis.

Yufei Huang, Le Qin, Haiying Lv, Shimeng Lv, Yong Lu

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Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yufei HuangDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Le QinDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Haiying LvDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shimeng LvSchool of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Yong LuDepartment of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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, I 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.

Indexed as

magnetic resonance imagingmeta-analysisneuronavigationPituitary neuroendocrine tumor (PitNET)

Identifiers

PMID39022256
PMCPMC11250324

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.