Frameless Robotic-Assisted Biopsy of Pediatric Brainstem Lesions: A Systematic Review and Meta-Analysis of Efficacy and Safety.


Journal

World neurosurgery
ISSN: 1878-8769
Titre abrégé: World Neurosurg
Pays: United States
ID NLM: 101528275

Informations de publication

Date de publication:
01 2023
Historique:
received: 10 09 2022
revised: 18 10 2022
accepted: 19 10 2022
pubmed: 29 10 2022
medline: 4 1 2023
entrez: 28 10 2022
Statut: ppublish

Résumé

Pediatric brainstem lesions are diagnoses that require tissue sampling to advance our understanding of them and their management. Frameless, robot-assisted biopsy of these lesions has emerged as a novel, viable biopsy approach. Correspondingly, the aim of this study was to quantitively and qualitatively summarize the contemporary literature regarding the likelihood of achieving tumor diagnosis and experiencing any postoperative complications. Searches of 7 electronic databases from inception to September 2022 were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were screened against prespecified criteria. Outcomes were pooled by random-effects meta-analyses of proportions where possible. A total of 8 cohort studies satisfied all criteria. They described 99 pediatric patients with brainstem lesions in whom frameless, robot-assisted biopsy was involved in their work-up. There were 62 (63%) male and 37 (37%) female patients with a median age of 9 years at time of biopsy. Overall, all patients had sufficient tissue obtained by initial biopsy for evaluation. Pooled estimate of achieving tumor diagnosis was 100% (95% confidence interval [CI] 97%-100%) across all studies with a high degree of certainty. Across all studies, there were no cases of procedure-related mortality. The pooled estimates of transient and permanent complications after biopsy were 10% (95% CI 4%-19%) and 0% (95% CI 0%-2%), respectively, of very low and low degrees of certainty each. The contemporary metadata demonstrates the frameless, robot-assisted biopsy of pediatric brainstem lesions is both effective and safe when performed in an experienced setting. Further research is needed to augment robot and automated technologies into workup algorithms.

Sections du résumé

BACKGROUND
Pediatric brainstem lesions are diagnoses that require tissue sampling to advance our understanding of them and their management. Frameless, robot-assisted biopsy of these lesions has emerged as a novel, viable biopsy approach. Correspondingly, the aim of this study was to quantitively and qualitatively summarize the contemporary literature regarding the likelihood of achieving tumor diagnosis and experiencing any postoperative complications.
METHODS
Searches of 7 electronic databases from inception to September 2022 were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were screened against prespecified criteria. Outcomes were pooled by random-effects meta-analyses of proportions where possible.
RESULTS
A total of 8 cohort studies satisfied all criteria. They described 99 pediatric patients with brainstem lesions in whom frameless, robot-assisted biopsy was involved in their work-up. There were 62 (63%) male and 37 (37%) female patients with a median age of 9 years at time of biopsy. Overall, all patients had sufficient tissue obtained by initial biopsy for evaluation. Pooled estimate of achieving tumor diagnosis was 100% (95% confidence interval [CI] 97%-100%) across all studies with a high degree of certainty. Across all studies, there were no cases of procedure-related mortality. The pooled estimates of transient and permanent complications after biopsy were 10% (95% CI 4%-19%) and 0% (95% CI 0%-2%), respectively, of very low and low degrees of certainty each.
CONCLUSIONS
The contemporary metadata demonstrates the frameless, robot-assisted biopsy of pediatric brainstem lesions is both effective and safe when performed in an experienced setting. Further research is needed to augment robot and automated technologies into workup algorithms.

Identifiants

pubmed: 36307039
pii: S1878-8750(22)01489-9
doi: 10.1016/j.wneu.2022.10.071
pii:
doi:

Types de publication

Meta-Analysis Systematic Review Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

87-93.e1

Informations de copyright

Copyright © 2022 Elsevier Inc. All rights reserved.

Auteurs

Victor M Lu (VM)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA. Electronic address: victor.lu@jhsmiami.org.

Stefan W Koester (SW)

Department of Neurosurgery, Vanderbilt University, Nashville, Tennessee, USA.

Long Di (L)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Turki Elarjani (T)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Evan M Luther (EM)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Daniel G Eichberg (DG)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Alexis A Morell (AA)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Christopher S Graffeo (CS)

Department of Neurosurgery, University of Oklahoma, Oklahoma City, Oklahoma, USA.

Othman Bin-Alamer (O)

Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.

Hussam Abou-Al-Shaar (H)

Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.

Ricardo J Komotar (RJ)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Michael E Ivan (ME)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

Ashish H Shah (AH)

Department of Neurological Surgery, University of Miami, Jackson Memorial Hospital, Miami, Florida, USA.

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