Evidence map›Paper›PMID 36481696›Full record

ArticleNeuroImage. Clinical2023

Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network.

Sanaz Nazari-Farsani, Yannan Yu, Rui Duarte Armindo, Maarten Lansberg, David S Liebeskind, Gregory Albers, Soren Christensen, Craig S Levin, Greg Zaharchuk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
–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

16 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

9 authors.

Sanaz Nazari-FarsaniDepartment of Radiology, Stanford University, CA, USA.
Yannan YuDepartment of Radiology, Stanford University, CA, USA; Internal Medicine Department, University of Massachusetts Memorial Medical Center, University of Massachusetts, Boston, USA.
Rui Duarte ArmindoDepartment of Radiology, Stanford University, CA, USA; Department of Neuroradiology, Hospital Beatriz Ângelo, Loures, Lisbon, Portugal.
Maarten LansbergDepartment of Neurology, Stanford University, CA, USA.
David S LiebeskindDepartment of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
Gregory AlbersDepartment of Neurology, Stanford University, CA, USA.
Soren ChristensenDepartment of Neurology, Stanford University, CA, USA.
Craig S LevinDepartment of Radiology, Stanford University, CA, USA.
Greg ZaharchukDepartment of Radiology, Stanford University, CA, USA. Electronic address: gregz@stanford.edu.

Funding

CTP to predict Response to recanalization in Ischemic Stroke Project (CRISP)R01NS075209 · NINDS · STANFORD UNIVERSITY · PI LANSBERG, MAARTEN G · 2011 to 2023
$6.0M
Predicting Tissue and Functional Outcome in Acute StrokeR01NS130172 · NINDS · STANFORD UNIVERSITY · PI Gregory George Zaharchuk · 2023 to 2026
$2.4M
NINDS NIH HHS R01 NS075209NINDS NIH HHS R01 NS130172
6 · The paper itself

Abstract

backgroundFor prognosis of stroke, measurement of the diffusion-perfusion mismatch is a common practice for estimating tissue at risk of infarction in the absence of timely reperfusion. However, perfusion-weighted imaging (PWI) adds time and expense to the acute stroke imaging workup. We explored whether a deep convolutional neural network (DCNN) model trained with diffusion-weighted imaging obtained at admission could predict final infarct volume and location in acute stroke patients.

methodsIn 445 patients, we trained and validated an attention-gated (AG) DCNN to predict final infarcts as delineated on follow-up studies obtained 3 to 7 days after stroke. The input channels consisted of MR diffusion-weighted imaging (DWI), apparent diffusion coefficients (ADC) maps, and thresholded ADC maps with values less than 620 × 10

resultsThe model obtained a median AUC of 0.91 (IQR: 0.84-0.96). After thresholding at an infarction probability of 0.5, the median sensitivity and specificity were 0.60 (IQR: 0.16-0.84) and 0.97 (IQR: 0.93-0.99), respectively, while the median DSC and absolute volume error were 0.50 (IQR: 0.17-0.66) and 27 ml (IQR: 7-60 ml), respectively. The model's predicted lesion volumes showed high correlation with ground truth volumes (ρ

conclusionAn AG-DCNN using diffusion information alone upon admission was able to predict infarct volumes at 3-7 days after stroke onset with comparable accuracy to models that consider both DWI and PWI. This may enable treatment decisions to be made with shorter stroke imaging protocols.

Indexed as

Brain IschemiaIschemic StrokeStrokeDiffusion Magnetic Resonance ImagingHumansInfarctionNeural Networks, ComputerAcute ischemic strokeDeep learningDWILesion segmentationMRIPWI

Identifiers

PMID36481696
PMCPMC9727698

What OpenQuestion holds

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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.