Evidence map›Paper›PMID 41024716›Full record

ArticleBrain and behavior2025

Automatic MRI Volumetry Assisted Visual Assessment of the Medial Temporal Lobe in Clinical Dementia Work-Up.

Karin Persson, Hanneke F M Rhodius-Meester, Trine Holt Edwin, Anne-Brita Knapskog, Peter Bekkhus-Wetterberg, Geir Selbæk, Knut Engedal, Till Schellhorn

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Article in Brain and behavior, 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Karin PerssonThe Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
Hanneke F M Rhodius-MeesterDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
Trine Holt EdwinDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
Anne-Brita KnapskogDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
Peter Bekkhus-WetterbergDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
Geir SelbækThe Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
Knut EngedalThe Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
Till SchellhornDivision of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.

Funding

Alzheimer NederlandHorizon 2022KLINBEFORSKMemorabel Dementia Fellowship 2021Nasjonalforeningen for FolkehelsenThe Southern and Eastern Norway Regional Health Authority
6 · The paper itself

Abstract

introductionEfficient and cost-effective diagnostic tools for supporting dementia assessment are increasingly important. We aimed to evaluate whether providing neuroradiologists with volumetric data from an automatic MRI software, NeuroQuant, enhanced the diagnostic accuracy of their visual MRI assessment.

methodsTwo neuroradiologists assessed brain MRIs from 366 patients (mean age 67.5 years, SD 9.2, and 52% females) with subjective cognitive decline (SCD, n 79), mild cognitive impairment (MCI, n 86), or dementia (n 201). The MCI and dementia patients were further diagnosed according to an etiology of Alzheimer's disease (AD, n 217) versus non-AD (n 70). In random order the neuroradiologists visually evaluated medial temporal lobe atrophy (MTA, scale 0-4) with and without having access to the NeuroQuant report of age and sex adjusted volumetric percentiles of the hippocampus. Receiver operating characteristics (ROCs) analyses were conducted to calculate the area under the curves (AUCs) for the visual MTA, the automated NeuroQuant percentile, and the combined NeuroQuant-assisted MTA in discriminating dementia from SCD and AD from non-AD.

resultsThe AUC of the visual MTA for dementia versus SCD discrimination increased slightly but not significantly when the neuroradiologists were provided with NeuroQuant results (AUC 0.76-0.79, p 0.28). Yet, the isolated NeuroQuant evaluation reached the highest accuracy (AUC 0.85, p < 0.001), significantly better than the MTA assessment (p 0.002) and the NeuroQuant-assisted MTA (p 0.04). Only the isolated NeuroQuant assessment discriminated AD from non-AD (AUC 0.60, p 0.006).

conclusionOn the basis of our findings, we suggest an increased use of clinically approved automatic volumetry methods in radiological departments.

Indexed as

Cognitive DysfunctionDementiaMagnetic Resonance ImagingTemporal LobeAgedAged, 80 and overAlzheimer DiseaseAtrophyFemaleHippocampusHumansMaleMiddle AgedAlzheimer's diseasecognitive impairmentdementiaNeuroQuantvisual rating scale

Identifiers

PMID41024716
PMCPMC12480925

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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.