Evidence map›Paper›PMID 38371891›Full record

ArticlePolish journal of radiology2024

Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia.

Waldemar Leszczyński, Wojciech Kazimierczak, Adam Lemanowicz, Zbigniew Serafin

Open access · diamondAbstract read
In one paragraph

Article in Polish journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 98% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Waldemar LeszczyńskiDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
Wojciech KazimierczakDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
Adam LemanowiczDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
Zbigniew SerafinDepartment of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
Nicolaus Copernicus University · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Medical imaging is one of the main methods of diagnosing COVID-19, along with real-time reverse trans-cription-polymerase chain reaction (RT-PCR) tests. The purpose of the study was to analyse the texture parameters of chest X-rays (CXR) of patients suspected of having COVID-19. Material and methods: Texture parameters of the CXRs of 70 patients with symptoms typical of COVID-19 infection were analysed using LIFEx software. The regions of interest (ROIs) included each lung separately, for which 57 para-meters were tested. The control group consisted of 30 healthy, age-matched patients with no pathological findings in CXRs. Results: According to the ROC analysis, 13 of the tested parameters differentiate the radiological image of lungs with COVID-19 features from the image of healthy lungs: GLRLM_LRHGE (AUC 0.91); DISCRETIZED_Q3 (AUC 0.90); GLZLM_HGZE (AUC 0.90); GLRLM_HGRE (AUC 0.89); DISCRETIZED_mean (AUC 0.89); DISCRETIZED_Q2 (AUC 0.61); GLRLM_SRHGE (AUC 0.87); GLZLM_LZHGE (AUC 0.87); GLZLM_SZHGE (AUC 0.84); DISCRETIZED_Q1 (AUC 0.81); NGLDM_Coarseness (AUC 0.70); DISCRETIZED_std (AUC 0.64); CONVENTIONAL_Q2 (AUC 0.61). Conclusions: Selected texture parameters of radiological CXRs make it possible to distinguish COVID-19 features from healthy ones.

Indexed as

chest X-raysCOVID-19radiomicstexture analysisthorax

Identifiers

PMID38371891
PMCPMC10867972
OpenAlexW4391453563

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.