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Ultrasound Med Biol ; 47(4): 947-959, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33451815

RESUMO

The aim of this study was to identify a method for staging hepatic fibrosis using a non-invasive, rapid and inexpensive technique based on ultrasound morphologic hepatic features. A total of 215 patients with different liver diseases underwent B-mode (2-D brightness mode) ultrasonography, vibration-controlled transient elastography, 2-D shear wave elastography and measurement of the controlled attenuation parameter with transient elastography. B-Mode images of the anterior margin of the left lobe were obtained and processed with automatic Genoa Line Quantification (GLQ) software based on a neural network for staging liver fibrosis. The accuracy of GLQ was 90.6% during model training and 78.9% in 38 different patients with concordant elastometric measures. Receiver operating characteristic curve analysis of GLQ performance using vibration-controlled transient elastography as a reference yielded areas under the curves of 0.851 for F ≥ F1, 0.793 for F ≥ F2, 0.784 for F ≥ F3 and 0.789 for F ≥ F4. GLQ has the potential to be a rapid, easy-to-perform and tolerable method in the staging of liver fibrosis.


Assuntos
Técnicas de Imagem por Elasticidade , Cirrose Hepática/diagnóstico por imagem , Cirrose Hepática/patologia , Software , Área Sob a Curva , Biópsia , Técnicas de Imagem por Elasticidade/métodos , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Projetos Piloto , Curva ROC
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