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量化腰椎旁肌内脂肪:自动阈值模型的准确性和可靠性

Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models.

作者信息

Wesselink E O, Elliott J M, Pool-Goudzwaard A, Coppieters M W, Pevenage P P, Di Ieva A, Weber Ii K A

机构信息

Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences - Program Musculoskeletal Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.

出版信息

N Am Spine Soc J. 2024 Jan 24;17:100313. doi: 10.1016/j.xnsj.2024.100313. eCollection 2024 Mar.

Abstract

BACKGROUND

The reported level of lumbar paraspinal intramuscular fat (IMF) in people with low back pain (LBP) varies considerably across studies using conventional T- and T-weighted magnetic resonance imaging (MRI) sequences. This may be due to the different thresholding models employed to quantify IMF. In this study we investigated the accuracy and reliability of established (two-component) and novel (three-component) thresholding models to measure lumbar paraspinal IMF from T-weighted MRI.

METHODS

In this cross-sectional study, we included MRI scans from 30 people with LBP (50% female; mean (SD) age: 46.3 (15.0) years). Gaussian mixture modelling (GMM) and K-means clustering were used to quantify IMF bilaterally from the lumbar multifidus, erector spinae, and psoas major using two and three-component thresholding approaches (GMM; K-means; GMM; and K-means). Dixon fat-water MRI was used as the reference for IMF. Accuracy was measured using Bland-Altman analyses, and reliability was measured using ICC. The mean absolute error between thresholding models was compared using repeated-measures ANOVA and post-hoc paired sample t-tests (α = 0.05).

RESULTS

We found poor reliability for K-means (ICC ≤ 0.38), moderate to good reliability for K-means (ICC ≥ 0.68), moderate reliability for GMM (ICC ≥ 0.63) and good reliability for GMM (ICC ≥ 0.77). The GMM (p < .001) and three-component models (p < .001) had smaller mean absolute errors than K-means and two-component models, respectively. None of the investigated models adequately quantified IMF for psoas major (ICC ≤ 0.01).

CONCLUSIONS

The performance of automated thresholding models is strongly dependent on the choice of algorithms, number of components, and muscle assessed. Compared to Dixon MRI, the GMM performed better than K-means and three-component performed better than two-component models for quantifying lumbar multifidus and erector spinae IMF. None of the investigated models accurately quantified IMF for psoas major. Future research is needed to investigate the performance of thresholding models in a more heterogeneous clinical dataset and across different sites and vendors.

摘要

背景

在使用传统T加权和质子密度加权磁共振成像(MRI)序列的研究中,报道的腰痛(LBP)患者腰椎旁肌内脂肪(IMF)水平差异很大。这可能是由于用于量化IMF的不同阈值模型所致。在本研究中,我们调查了既定的(双组分)和新型的(三组分)阈值模型从T加权MRI测量腰椎旁IMF的准确性和可靠性。

方法

在这项横断面研究中,我们纳入了30例LBP患者的MRI扫描(50%为女性;平均(标准差)年龄:46.3(15.0)岁)。使用高斯混合模型(GMM)和K均值聚类,通过双组分和三组分阈值方法(GMM;K均值;GMM;和K均值)从腰大肌、竖脊肌和腰方肌双侧量化IMF。Dixon脂肪-水MRI用作IMF的参考。使用Bland-Altman分析测量准确性,使用ICC测量可靠性。使用重复测量方差分析和事后配对样本t检验(α = 0.05)比较阈值模型之间的平均绝对误差。

结果

我们发现K均值的可靠性较差(ICC≤0.38),K均值的可靠性为中等至良好(ICC≥0.68),GMM的可靠性为中等(ICC≥0.63),GMM的可靠性为良好(ICC≥0.77)。GMM(p <.001)和三组分模型(p <.001)的平均绝对误差分别小于K均值和双组分模型。所研究的模型均未充分量化腰大肌的IMF(ICC≤0.01)。

结论

自动阈值模型的性能强烈依赖于算法的选择、组分数量和所评估的肌肉。与Dixon MRI相比,在量化腰大肌和竖脊肌IMF方面,GMM的表现优于K均值,三组分模型的表现优于双组分模型。所研究的模型均未准确量化腰大肌的IMF。未来需要开展研究,以调查阈值模型在更具异质性的临床数据集中以及不同部位和供应商之间的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d73/10869289/d1b088af529b/gr1.jpg

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