Evidence map›Paper›PMID 36572984›Full record

ArticleInterventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences2025

Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.

Iris E Chen, Brian Tsui, Haoyue Zhang, Joe X Qiao, William Hsu, May Nour, Noriko Salamon, Luke Ledbetter, Jennifer Polson, Corey Arnold and 6 more

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

Article in Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Safety and efficacy of endovascular treatment for acute ischemic stroke of large-vessel occlusion beyond the time window based on imaging evaluation.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2026
    Article
  4. Methinks AI software for identifying large vessel occlusion in non-contrast head CT: A pilot retrospective study in American population.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025
    Article
  5. Article
  6. Article
  7. Review
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 1 institution in 1 country.

Iris E ChenDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.ORCID 0000-0003-1394-6543
Brian TsuiDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Haoyue ZhangDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Joe X QiaoDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
William HsuDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
May NourDepartment of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Luke LedbetterDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Jennifer PolsonDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Corey ArnoldDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Mersedeh BahrHossieniDepartment of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Reza JahanDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Gary DuckwilerDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Jeffrey SaverDepartment of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
David LiebeskindDepartment of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
Kambiz NaelDepartment of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA.
University of California, Los Angeles · US

Funding

Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image FeaturesR56EB031993 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HSU, WILLIAM, MCNITT-GRAY, MICHAEL F · 2021 to 2021
$612k
NIBIB NIH HHS R56 EB031993
6 · The paper itself

Abstract

backgroundAccurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine noncontrast computed tomography (NCCT).

objectiveWe used an ML-trained algorithm to quantify ischemic core volume on NCCT in a comparative analysis to pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with AIS.

methodsPatients with AIS who had both pretreatment NCCT and MRI were enrolled. An automatic segmentation ML approach was applied using Brainomix software (Oxford, UK) to segment the ischemic voxels and calculate ischemic core volume on NCCT. Ischemic core volume was also calculated on baseline MRI DWI. Comparative analysis was performed using Bland-Altman plots and Pearson correlation.

resultsA total of 72 patients were included. The time-to-stroke onset time was 134.2/89.5 minutes (mean/median). The time difference between NCCT and MRI was 64.8/44.5 minutes (mean/median). In patients who presented within 1 hour from stroke onset, the ischemic core volumes were significantly (p  =  0.005) underestimated by ML-NCCT. In patients presented beyond 1 hour, the ML-NCCT estimated ischemic core volumes approximated those obtained by MRI-DWI and with significant correlation (

conclusionThe ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.

Indexed as

Brain IschemiaIschemic StrokeMachine LearningTomography, X-Ray ComputedAgedAged, 80 and overAlgorithmsDiffusion Magnetic Resonance ImagingFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesAcute ischemic strokemachine learningnoncontrast CT

Identifiers

PMID36572984
PMCPMC11833852
OpenAlexW4312200223

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.