Cargando…
Machine learning analysis of adrenal lesions: preliminary study evaluating texture analysis in the differentiation of adrenal lesions
PURPOSE: This study aimed to determine the accuracy of texture analysis in differentiating adrenal lesions on unenhanced computed tomography (CT) images. METHODS: In this single-center retrospective study, 166 adrenal lesions in 140 patients (64 women, 76 men; mean age 56.58 ± 13.65 years) were eval...
Autores principales: | Altay, Canan, Başara Akın, Işıl, Özgül, Abdullah Hakan, Adıyaman, Süleyman Cem, Yener, Abdullah Serkan, Seçil, Mustafa |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Galenos Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679711/ https://www.ncbi.nlm.nih.gov/pubmed/36987841 http://dx.doi.org/10.5152/dir.2022.21266 |
Ejemplares similares
-
Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD
por: Xu, Xinxin, et al.
Publicado: (2020) -
Sonoelastography findings of myofibroblastoma
por: Ozgul, Hakan Abdullah, et al.
Publicado: (2022) -
Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning
por: Ryu, Hwaseong, et al.
Publicado: (2021) -
Use of shear-wave elastography to distinguish complex and complicated fibroadenomas from simple fibroadenomas
por: Başara Akın, Işıl, et al.
Publicado: (2023) -
Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time
por: Jing, Xueping, et al.
Publicado: (2022)