Evidence map›Paper›PMID 36472536›Full record

ArticleRadiology2023

Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning.

Yannan Yu, Soren Christensen, Jiahong Ouyang, Fabien Scalzo, David S Liebeskind, Maarten G Lansberg, Gregory W Albers, Greg Zaharchuk

Open access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
4.2field-weighted citation impact, top 5% 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Quantitative imaging outperforms No-reflow in predicting functional outcomes in a translational stroke model.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025
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  11. Non-Contrasted CT Radiomics for SAH Prognosis Prediction.Bioengineering (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Yannan YuFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0002-1718-6505
Soren ChristensenFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0003-1242-3724
Jiahong OuyangFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).
Fabien ScalzoFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0001-9755-8104
David S LiebeskindFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0002-5109-8736
Maarten G LansbergFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0002-3545-6927
Gregory W AlbersFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0003-0263-4632
Greg ZaharchukFrom the Departments of Radiology (Y.Y., G.Z.), Neurology (S.C., M.G.L., G.W.A.), and Electrical Engineering (J.O.), Stanford University, 1201 Welch Rd, PS-04, Mailcode 5488, Stanford, CA 94305-5488; and Department of Neurology, University of California, Los Angeles, Los Angeles, Calif (F.S., D.S.L.).ORCID 0000-0001-5781-8848
Stanford University · US

Funding

Quantifying Collateral Perfusion in Cerebrovascular DiseaseR01NS066506 · NINDS · STANFORD UNIVERSITY · PI ZAHARCHUK, GREGORY GEORGE · 2009 to 2017
$4.9M
Predicting Tissue and Functional Outcome in Acute StrokeR01NS130172 · NINDS · STANFORD UNIVERSITY · PI Gregory George Zaharchuk · 2023 to 2026
$2.4M
NINDS NIH HHS R01 NS066506NINDS NIH HHS R01 NS130172
6 · The paper itself

Abstract

Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with a target mismatch profile from diffusion-weighted imaging (DWI) and clinical information alone, using perfusion MRI as the reference standard. Materials and Methods Imaging data sets of patients with acute ischemic stroke with baseline perfusion MRI and DWI were retrospectively reviewed from multicenter data available from 2008 to 2019 (Imaging Collaterals in Acute Stroke, Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2, and University of California, Los Angeles stroke registry). For perfusion MRI, rapid processing of perfusion and diffusion software automatically segmented the hypoperfusion lesion (time to maximum, ≥6 seconds) and ischemic core (apparent diffusion coefficient [ADC], ≤620 × 10

Indexed as

Brain IschemiaDeep LearningIschemic StrokeStrokeAgedDiffusion Magnetic Resonance ImagingHumansIschemiaMagnetic Resonance ImagingMaleRetrospective Studies

Identifiers

PMID36472536
PMCPMC10068889
OpenAlexW4310779494

What OpenQuestion holds

Textmetadata
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.