Assessment of agreement between manual and automated processing of liver MR elastography for shear stiffness estimation in children and young adults with autoimmune liver disease

评估手动和自动处理肝脏磁共振弹性成像数据在自身免疫性肝病患儿和青少年剪切刚度估计方面的一致性

阅读:1

Abstract

PURPOSE: To compare automated versus standard of care manual processing of 2D gradient recalled echo (GRE) liver MR Elastography (MRE) in children and young adults. MATERIALS AND METHODS: 2D GRE liver MRE data from research liver MRI examinations performed as part of an autoimmune liver disease registry between March 2017 and March 2020 were analyzed retrospectively. All liver MRE data were acquired at 1.5 T with 60 Hz mechanical vibration frequency. For manual processing, two independent readers (R1, R2) traced regions of interest on scanner generated shear stiffness maps. Automated processing was performed using MREplus+ (Resoundant Inc.) using 90% (A90) and 95% (A95) confidence masks. Agreement was evaluated using intra-class correlation coefficients (ICC) and Bland-Altman analyses. Classification performance was evaluated using receiver operating characteristic curve (ROC) analyses. RESULTS: In 65 patients with mean age of 15.5 ± 3.8 years (range 8-23 years; 35 males) median liver shear stiffness was 2.99 kPa (mean 3.55 ± 1.69 kPa). Inter-reader agreement for manual processing was very strong (ICC = 0.99, mean bias = 0.01 kPa [95% limits of agreement (LoA): - 0.41 to 0.44 kPa]). Correlation between manual and A95 automated processing was very strong (R1: ICC = 0.988, mean bias = 0.13 kPa [95% LoA: - 0.40 to 0.68 kPa]; R2: ICC = 0.987, mean bias = 0.13 kPa [95% LoA: - 0.44 to 0.69 kPa]). Automated measurements were perfectly replicable (ICC = 1.0; mean bias = 0 kPa). CONCLUSION: Liver shear stiffness values obtained using automated processing showed excellent agreement with manual processing. Automated processing of liver MRE was perfectly replicable.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。