Evidence map›Paper›PMID 39283980›Full record

ArticleNeuro-oncology2025

Dynamics of tumor evolution after Gamma Knife radiosurgery for sporadic vestibular schwannoma: Defining volumetric patterns characterizing individual trajectory.

Anne Balossier, Madalina Olteanu, Christine Delsanti, Lucas Troude, Jean-Marc Thomassin, Pierre-Hugues Roche, Marie Chavent, Jean Régis

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Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Anne BalossierAix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.ORCID 0000-0001-9858-3766
Madalina OlteanuCEREMADE, UMR 7534, Université Paris Dauphine PSL, Paris, France.
Christine DelsantiFunctional and Stereotactic Neurosurgery, AP-HM, Timone Hospital, Marseille, France.
Lucas TroudeDepartment of Neurosurgery, AP-HM, North University Hospital, Marseille, France.
Jean-Marc ThomassinDepartment of Head and Neck Surgery, AP-HM, Timone Hospital, Marseille, France.
Pierre-Hugues RocheDepartment of Neurosurgery, AP-HM, North University Hospital, Marseille, France.
Marie ChaventUMR5251, INRIA, Université de Bordeaux, Talence, France.
Jean RégisAix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.

Funding

French Society of Neurosurgery
6 · The paper itself

Abstract

backgroundThe definition of tumor control and treatment failure after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma (VS) is still debated. The lack of knowledge on the dynamics of tumor evolution can lead to misinterpretation and subsequent inappropriate second treatment. The aim of this study was to evaluate the post-GKRS dynamics of the evolution of tumor volume and characterize volumetric patterns.

methodsWe included patients with sporadic VS treated by GKRS with an MRI follow-up of a minimum of 3 years. A clustering was performed in 2 steps: Definition of the patterns of evolution based on a subset of patients with the most comprehensive follow-up, and then the assignment of the remaining patients on a best-fit basis. The minimum length of follow-up was assessed by measuring the consistency of the clusters over time (adjusted rand index and normalized mutual information). An analysis of the discriminant variables was finally performed.

resultsA total of 1607 patients were included (median follow-up: 67 months). Five patterns were defined with 1 pattern gathering almost all cases of treatment failure. The clustering at 5 years afforded the highest consistency with long-term follow-up. Discriminant variables for clusters were as follows: sex, initial symptoms, delay of diagnosis, Koos grading, fundus invasion, and number of isocenters.

conclusionsThe definition of these robust distinct patterns is likely to help the physicians tremendously to distinguish tumor control from potential failure. We advocate for no retreatment decision before 5 years post-GKRS. Further investigations are required to decide if the dynamics of evolution can be predicted at GKRS on an individual basis.

Indexed as

Neuroma, AcousticRadiosurgeryAdultAgedAged, 80 and overFemaleFollow-Up StudiesHumansMagnetic Resonance ImagingMaleMiddle AgedPrognosisTumor BurdenYoung Adultfailurepatternsradiosurgeryvestibular schwannomavolumetry

Identifiers

PMID39283980
PMCPMC11812029

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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.