医学
生物标志物
动脉瘤
内科学
放射科
神经血管束
置信区间
病理
生物
生物化学
作者
Jiabin Su,Jing Cao,Heng Yang,Wei Xu,Wanshan Liu,Ruimin Wang,Yida Huang,Jiao Wu,Xinjie Gao,Ruiyuan Weng,Jun Pu,Ning Liu,Yuxiang Gu,Kun Qian,Wei Ni
标识
DOI:10.1002/smtd.202201486
摘要
Abstract Unruptured intracranial aneurysm (UIA) is a high‐risk cerebrovascular saccular dilatation, the effective medical management of which depends on high‐performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time‐consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high‐performance UIA‐specific serum metabolic fingerprints. Diagnostic performance with an area‐under‐the‐curve (AUC) of 0.842 (95% confidence interval (CI): 0.783‐0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3‐methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727‐0.897) and effective growth risk assessment ( p <0.05, two‐tailed t‐test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.
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