MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

Autor:	Xiaokai Mo, Wenbo Chen, Simin Chen, Zhuozhi Chen, Yuanshu Guo, Yulian Chen, Xuewei Wu, Lu Zhang, Qiuying Chen, Zhe Jin, Minmin Li, Luyan Chen, Jingjing You, Zhiyuan Xiong, Bin Zhang, Shuixing Zhang
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Chronic renal insufficiency Glomerular filtration rate Magnetic resonance imaging Texture analysis Machine learning Medical physics. Medical radiology. Nuclear medicine R895-920
Zdroj:	Insights into Imaging, Vol 14, Iss 1, Pp 1-9 (2023)
Druh dokumentu:	article
ISSN:	1869-4101
DOI:	10.1186/s13244-023-01370-4
Popis:	Key points 1. Texture analysis based on coronal T2-weighted MR images could evaluate the renal function in patients with diabetes. 2. The All-K and LC-K outperformed other segmentation methods in the evaluation of renal function impairment. 3. The segmentation methods could affect the results of renal function evaluation and the integrity of the coronal slices was crucial for renal imaging texture analysis.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/498e9ccd062d4d8b9a5ddf678725767d Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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