Evidence map›Paper›PMID 35213340›Full record

ArticleJournal of X-ray science and technology2022

Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients.

Gopichandh Danala, Bappaditya Ray, Masoom Desai, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Sai Kiran R Maryada, Bin Zheng

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

Article in Journal of X-ray science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 3 institutions in 1 country.

Gopichandh DanalaSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
Bappaditya RayUT Southwestern Medical Center, Dallas, TX, USA.
Masoom DesaiDepartment of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA.
Morteza HeidariSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
Seyedehnafiseh MirniaharikandeheiSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
Sai Kiran R MaryadaSchool of Computer Sciences, University of Oklahoma, Norman, OK, USA.
Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
University of Oklahoma · USSouthwestern Medical Center · USUniversity of Oklahoma Medical Center · US

Funding

Use of 3D Quantitative Optical Methods to Optimize Mebendazole Treatment of Ovarian CancerP20GM135009 · NIGMS · UNIVERSITY OF OKLAHOMA · PI Yuye Ling · 2022 to 2026
$13.6M
Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019
$2.5M
NCI NIH HHS R01 CA197150NIGMS NIH HHS P20 GM135009
6 · The paper itself

Abstract

backgroundEndovascular mechanical thrombectomy (EMT) is an effective method to treat acute ischemic stroke (AIS) patients due to large vessel occlusion (LVO). However, stratifying AIS patients who can and cannot benefit from EMT remains a clinical challenge.

objectiveTo develop a new quantitative image marker computed from pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among AIS patients undergoing EMT after diagnosis of LVO.

methodsA retrospective dataset of 31 AIS patients with pre-intervention CTP images is assembled. A computer-aided detection (CAD) scheme is developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and machine learning (ML) models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. Performance of image markers is evaluated using the area under the ROC curve (AUC) and accuracy computed from the confusion matrix.

resultsThe ML model using the neuroimaging features computed from the slopes of the subtracted cumulative blood flow curves between two cerebral hemispheres yields classification performance of AUC = 0.878±0.077 with an overall accuracy of 90.3%.

conclusionsThis study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients' prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients.

Indexed as

Ischemic StrokeComputed Tomography AngiographyHumansPrognosisRetrospective StudiesTomography, X-Ray ComputedAcute ischemic stroke (AIS)computer-aided detection and diagnosis (CAD)prediction of AIS prognosisquantitative image markers

Identifiers

PMID35213340
PMCPMC9097354
OpenAlexW4214595549

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.