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使用具有回波不对称性和最小二乘估计量化序列成像的水脂迭代分解技术对2型糖尿病患者肾脂肪变性进行定量评估:可重复性及临床意义

Quantitative assessment of renal steatosis in patients with type 2 diabetes mellitus using the iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence imaging: repeatability and clinical implications.

作者信息

Liu Jian, Wu Yu, Tian Chong, Zhang Xunlan, Su Zhijie, Nie Lisha, Wang Rongpin, Zeng Xianchun

机构信息

Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, China.

出版信息

Quant Imaging Med Surg. 2024 Oct 1;14(10):7341-7352. doi: 10.21037/qims-24-330. Epub 2024 Sep 23.

Abstract

BACKGROUND

Fatty kidney disease is linked to renal function damage, but there is no noninvasive tool for monitoring renal fat accumulation. This study aimed to explore the repeatability of the iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification (IDEAL-IQ) sequence imaging in quantifying renal fat deposition and to assess the differences observed in patients with type 2 diabetes mellitus (T2DM).

METHODS

A total of 26 healthy participants underwent two IDEAL-IQ scans without repositioning, and the repeatability of the imaging technique was assessed with Bland-Altman analysis. Additionally, 96 patients with T2DM underwent a single IDEAL-IQ scan for the examination of renal fat deposition. The patients with T2DM were classified into three groups based on their estimated glomerular filtration rate (eGFR). One-way analysis of variance was used to analyze the differences of renal fat depositions between the groups. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of IDEAL-IQ.

RESULTS

Bland-Altman analyses showed narrower limits of agreement and a significant correlation (r=0.81; P<0.05) between the two IDEAL-IQ scans. Statistically significant differences between the healthy volunteers and patients with T2DM, diabetic kidney disease (DKD) I-II, and or DKD III-IV were found in renal parenchymal proton-density fat fraction (PDFF) values (P<0.001). Renal parenchymal PDFF was negatively correlated with eGFR (r=-0.437; P<0.001) and positive correlated with serum creatinine level (µmol/L) (r=0.421; P<0.001). The area under the curve of IDEAL-IQ in discriminating between the healthy volunteers and patients with T2DM was 0.857. For discriminating T2DM from DKD I-II and DKD III-IV, the IDEAL-IQ had an area under the curve of 0.689 and 0.823, respectively.

CONCLUSIONS

IDEAL-IQ is a promising and reproducible technique for the assessment of renal fat deposition and identification of risk of DKD in patients with T2DM. Moreover, IDEAL-IQ imaging is expected to improve the sensitivity and specificity of early renal function damage and staging assessment of patients with T2DM.

摘要

背景

脂肪性肾病与肾功能损害有关,但尚无用于监测肾脂肪蓄积的无创工具。本研究旨在探讨采用回波不对称与最小二乘估计量化迭代分解水脂成像(IDEAL-IQ)序列成像在量化肾脂肪沉积方面的可重复性,并评估2型糖尿病(T2DM)患者的观察差异。

方法

26名健康参与者在未重新定位的情况下接受了两次IDEAL-IQ扫描,采用Bland-Altman分析评估成像技术的可重复性。此外,96名T2DM患者接受了一次IDEAL-IQ扫描以检查肾脂肪沉积情况。根据估计的肾小球滤过率(eGFR)将T2DM患者分为三组。采用单因素方差分析分析各组间肾脂肪沉积的差异。采用受试者工作特征曲线分析评估IDEAL-IQ的诊断性能。

结果

Bland-Altman分析显示两次IDEAL-IQ扫描之间的一致性界限较窄且具有显著相关性(r=0.81;P<0.05)。健康志愿者与T2DM患者、糖尿病肾病(DKD)I-II期患者以及DKD III-IV期患者之间的肾实质质子密度脂肪分数(PDFF)值存在统计学显著差异(P<0.001)。肾实质PDFF与eGFR呈负相关(r=-0.437;P<0.001),与血清肌酐水平(µmol/L)呈正相关(r=0.421;P<0.001)。IDEAL-IQ在区分健康志愿者与T2DM患者时的曲线下面积为0.857。在区分T2DM与DKD I-II期以及DKD III-IV期时,IDEAL-IQ的曲线下面积分别为0.689和0.823。

结论

IDEAL-IQ是一种有前景且可重复的技术,用于评估肾脂肪沉积以及识别T2DM患者的DKD风险。此外,预计IDEAL-IQ成像可提高T2DM患者早期肾功能损害和分期评估的敏感性和特异性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d15c/11485345/18b7263e469b/qims-14-10-7341-f1.jpg

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