Evidence map›Paper›PMID 42479243›Full record

ReviewActa neurochirurgica2026

Moving on in hydrocephalus imaging: from 2D to 3D biomarkers.

Raffaele Da Mutten, Rafael Turczynski Holmgren, Erik Edström, Adrian Elmi-Terander, Victor Egon Staartjes

Abstract readReview
In one paragraph

Review in Acta neurochirurgica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Raffaele Da MuttenMachine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Rafael Turczynski HolmgrenDepartment of Neurosurgery, Linköping University, Linköping, Sweden.
Erik EdströmDepartment of Clinical Neuroscience, Karolinska Institute, Nobels Väg 6, Solna, 171 77, Stockholm, Sweden.
Adrian Elmi-TeranderDepartment of Clinical Neuroscience, Karolinska Institute, Nobels Väg 6, Solna, 171 77, Stockholm, Sweden.
Victor Egon StaartjesMachine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland. victor.staartjes@ki.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLinear two-dimensional indices such as the Evans index, callosal angle, and fronto-occipital horn ratio remain the clinical standard for hydrocephalus assessment, yet are limited by measurement variability, insensitivity to spatial CSF redistribution, and reduced sensitivity to volumetric change over time. This narrative review aims to summarize established two-dimensional indices, their structural limitations, and describe how automated segmentation and radiomic feature extraction enable three-dimensional assessment across four clinical domains.

methodsA narrative literature review was conducted using PubMed. Studies addressing hydrocephalus imaging biomarkers, ventricular volumetry, automated segmentation, radiomics, and machine learning applications were reviewed. Reference lists of relevant articles were hand-searched for additional sources. The literature was synthesized across four clinical domains: pediatric hydrocephalus monitoring, differential diagnosis of ventriculomegaly, preoperative prediction of response to cerebrospinal fluid diversion, and longitudinal post-treatment follow-up.

resultsAcross pediatric hydrocephalus monitoring, differential diagnosis of ventriculomegaly, preoperative prediction of response to CSF diversion, and post-shunt longitudinal follow-up, three-dimensional volumetric and radiomic approaches consistently outperform linear indices. Machine learning models report AUCs exceeding 0.9 for differential diagnosis and shunt response prediction. Automated segmentation has reached excellent performance for detection tasks, and volumetry is more sensitive to postoperative change than the Evans index.

conclusionsDespite strong metric performance, clinical translation remains limited by small single-centre datasets, missing external validation, and undefined thresholds for clinically meaningful volumetric change. Embedding validated tools into radiological workflows and clinical guidelines will be essential before three-dimensional biomarkers can improve routine hydrocephalus care.

Indexed as

HydrocephalusImaging, Three-DimensionalNeuroimagingBiomarkersCerebrospinal Fluid ShuntsHumansMachine LearningMagnetic Resonance ImagingRadiomicsBiomarkers3DBiomarkersHydrocephalusImagingSegmentationVolumetry

Identifiers

PMID42479243
PMCPMC13400597

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Registered trials

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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.