Evidence map›Paper›PMID 37554636›Full record

ArticleFrontiers in neuroimaging2023

Manual lesion segmentations for traumatic brain injury characterization.

Alexis Bennett, Rachael Garner, Michael D Morris, Marianna La Rocca, Giuseppe Barisano, Ruskin Cua, Jordan Loon, Celina Alba, Patrick Carbone, Shawn Gao and 6 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

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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

16 authors at 3 institutions in 2 countries.

Alexis BennettUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Rachael GarnerUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Michael D MorrisDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Marianna La RoccaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Giuseppe BarisanoUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Ruskin CuaUSC Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Jordan LoonUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Celina AlbaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Patrick CarboneUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Shawn GaoUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Asenat PantojaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Azrin KhanUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Noor NouailiUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Paul VespaDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Arthur W TogaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Dominique DuncanUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
University of Southern California · USUniversity of California, Los Angeles · USUniversity of Bari Aldo Moro · IT

Funding

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Public Engagement CoreU54NS100064 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2017 to 2021
$21.7M
SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic EpilepsyR01NS111744 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DUNCAN, DOMINIQUE, ERDOGMUS, DENIZ · 2019 to 2022
$981k
NINDS NIH HHS U54 NS100064
6 · The paper itself

Abstract

Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis

Indexed as

epilepsylesion maskmagnetic resonance imagingpost traumatic seizuressegmentationtraumatic brain injury

Identifiers

PMID37554636
PMCPMC10406209
OpenAlexW4327617092

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.