Evidence map›Paper›PMID 40892299›Full record

ArticleActa neurochirurgica2025

Role of mixed reality in the workflow of complex neurooncological surgeries: a case analysis in thalamic surgery.

Luca Zanuttini, Elisa Colombo, Grazia Menna, Victor E Staartjes, Tristan van Doormaal, Niklaus Krayenbühl, Luca Regli, Carlo Serra

Abstract read
In one paragraph

Article in Acta neurochirurgica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Luca ZanuttiniMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Elisa ColomboDepartment of Neurosurgery, University Hospital Zürich, Zurich, Switzerland.
Grazia MennaMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Victor E StaartjesMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Tristan van DoormaalDepartment of Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands.
Niklaus KrayenbühlDepartment of Neurosurgery, University Hospital Zürich, Zurich, Switzerland.
Luca RegliDepartment of Neurosurgery, University Hospital Zürich, Zurich, Switzerland.
Carlo SerraMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland. carlo.serra@usz.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrosurgical resection of thalamic tumors requires precise anatomical knowledge and meticulous preoperative planning. Given the complexity of thalamic surgery, selecting an optimal surgical approach demands an accurate three-dimensional understanding of relevant structures. Advanced imaging post-processing, including three-dimensional (3D) model construction, can aid surgical planning and mental rehearsal of the procedure. The integration of Mixed Reality (MxR) with interactive holograms may further enhance anatomical clarity, improve risk assessment, and facilitate safer and more extensive tumor resection.

methodsThis retrospective study analyzed patients who underwent thalamic tumor resection with holographic surgical planning between 2022 and 2024. Key anatomical structures, particularly those delimiting the four "free thalamic surfaces", in contact with cerebrospinal fluid spaces, were segmented using post-processing software (Lumi and 3D Slicer) based on volumetric MRI sequences. The resulting patient-specific holograms were utilized preoperatively to simulate and select the optimal surgical approach and intraoperatively to verify its suitability. Demographic, clinical, radiological, and perioperative data were collected.

resultsTen surgical procedures were performed in nine patients, including seven neuroepithelial tumors and two metastases. All critical structures were successfully segmented, allowing effective surgical simulation and approach selection (8 Anterior Interhemispheric Transcallosal, 2 Perimedian Supracerebellar Transtentorial approaches). The additional planning time for segmentation averaged 45 min. The mean extent of resection achieved was 94.88% (range: 78.6%-100%). At 3-months postoperative follow-up one out of nine patients experienced a permanent new neurological deficit due to surgery.

conclusionIntegrating MxR into the preoperative workflow for microsurgical removal of complex thalamic tumors proved to be a valuable tool for surgical planning, risk assessment, and intraoperative guidance. The additional preparation time required for holographic simulation appears justified given the anatomical complexity of thalamic lesions.

Indexed as

Brain NeoplasmsMicrosurgeryNeoplasms, NeuroepithelialNeurosurgical ProceduresThalamusWorkflowAdultAgedFemaleHolographyHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesMicroneurosurgeryMxRThalamic gliomaThalamus

Identifiers

PMID40892299
PMCPMC12405398

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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.