医学
肾脏疾病
磁共振成像
肾源性系统性纤维化
超声波
放射科
纤维化
病理
内科学
作者
Shanshan Wan,Shiping Wang,Xinyu He,Chao Song,Jia-Ping Wang
出处
期刊:Renal Failure
[Informa]
日期:2024-06-28
卷期号:46 (2): 2367021-2367021
被引量:5
标识
DOI:10.1080/0886022x.2024.2367021
摘要
Researchers have delved into noninvasive diagnostic methods of renal fibrosis (RF) in chronic kidney disease, including ultrasound (US), magnetic resonance imaging (MRI), and radiomics. However, the value of these diagnostic methods in the noninvasive diagnosis of RF remains contentious. Consequently, the present study aimed to systematically delineate the accuracy of the noninvasive diagnosis of RF. A systematic search covering PubMed, Embase, Cochrane Library, and Web of Science databases for all data available up to 28 July 2023 was conducted for eligible studies. We included 21 studies covering 4885 participants. Among them, nine studies utilized US as a noninvasive diagnostic method, eight studies used MRI, and four articles employed radiomics. The sensitivity and specificity of US for detecting RF were 0.81 (95% CI: 0.76–0.86) and 0.79 (95% CI: 0.72–0.84). The sensitivity and specificity of MRI were 0.77 (95% CI: 0.70–0.83) and 0.92 (95% CI: 0.85–0.96). The sensitivity and specificity of radiomics were 0.69 (95% CI: 0.59–0.77) and 0.78 (95% CI: 0.68–0.85). The current early noninvasive diagnostic methods for RF include US, MRI, and radiomics. However, this study demonstrates that US has a higher sensitivity for the detection of RF compared to MRI. Compared to US, radiomics studies based on US did not show superior advantages. Therefore, challenges still exist in the current radiomics approaches for diagnosing RF, and further exploration of optimized artificial intelligence (AI) algorithms and technologies is needed.
科研通智能强力驱动
Strongly Powered by AbleSci AI