Evidence map›Paper›PMID 39350771›Full record

ArticleFrontiers in neuroimaging2024

Quantitative reliability assessment of brain MRI volumetric measurements in type II GM1 gangliosidosis patients.

Christopher Zoppo, Josephine Kolstad, Jean Johnston, Precilla D'Souza, Anna Luisa Kühn, Zeynep Vardar, Ahmet Peker, Clifford Lindsay, Zubir S Rentiya, Robert King and 5 more

Abstract read
In one paragraph

Article in Frontiers in neuroimaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
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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

15 authors.

Christopher Zoppo *Image Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Josephine Kolstad *Image Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Jean JohnstonNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States.
Precilla D'SouzaNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States.
Anna Luisa KühnImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Zeynep VardarImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Ahmet PekerImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Clifford LindsayImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Zubir S RentiyaImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Robert KingImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Heather Gray-EdwardsHorae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Behroze VachhaImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Maria T AcostaNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States.
Cynthia J TifftNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States.
Mohammed Salman ShazeebImage Processing and Analysis Core (iPAC), Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: GM1-gangliosidosis (GM1) leads to extensive neurodegenerative changes and atrophy that precludes the use of automated MRI segmentation techniques for generating brain volumetrics. We developed a standardized segmentation protocol for brain MRIs of patients with type II GM1 and then assessed the inter- and intra-rater reliability of this methodology. The volumetric data may be used as a biomarker of disease burden and progression, and standardized methodology may support research into the natural history of the disease which is currently lacking in the literature. Approach: Twenty-five brain MRIs were included in this study from 22 type II GM1 patients of which 8 were late-infantile subtype and 14 were juvenile subtype. The following structures were segmented by two rating teams on a slice-by-slice basis: whole brain, ventricles, cerebellum, lentiform nucleus, thalamus, corpus callosum, and caudate nucleus. The inter- and intra-rater reliability of the segmentation method was assessed with an intraclass correlation coefficient as well as Sorensen-Dice and Jaccard coefficients. Results: Based on the Sorensen-Dice and Jaccard coefficients, the inter- and intra-rater reliability of the segmentation method was significantly better for the juvenile patients compared to late-infantile ( Conclusions: The standardized segmentation approach described here has good inter- and intra-rater reliability and may provide greater accuracy and reproducibility for neuromorphological studies in this group of patients and help to further expand our understanding of the natural history of this disease.

Indexed as

brain MRIGM1 gangliosidosisinter/intra-rater reliabilitysegmentationvolumetrics

Identifiers

PMID39350771
PMCPMC11440193

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