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Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia.
Leszczynski, Waldemar; Kazimierczak, Wojciech; Lemanowicz, Adam; Serafin, Zbigniew.
Afiliación
  • Leszczynski W; Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland.
  • Kazimierczak W; Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland.
  • Lemanowicz A; Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland.
  • Serafin Z; Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland.
Pol J Radiol ; 89: e49-e53, 2024.
Article en En | MEDLINE | ID: mdl-38371891
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.
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Pol J Radiol Año: 2024 Tipo del documento: Article País de afiliación: Polonia

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Pol J Radiol Año: 2024 Tipo del documento: Article País de afiliación: Polonia