髋臼软骨下骨的CT纹理分析可以区分正常髋关节和凸轮阳性髋关节

CT texture analysis of acetabular subchondral bone can discriminate between normal and cam-positive hips

OBJECTIVES:The purpose of this study was to determine if the CT texture profile of acetabular subchondral bone differs between normal, asymptomatic cam-positive, and symptomatic cam-FAI hips. In addition, the utility of texture analysis to discriminate between the three hip statuses was explored using a machine learning approach.

METHODS:IRB-approved, case-control study analyzing CT images in subjects with and without cam morphology from August 2010 to December 2013. Sixty-eight subjects were included: 19 normal controls, 26 asymptomatic cam, and 23 symptomatic cam-FAI. Acetabular subchondral bone was contoured on the sagittal oblique CT images using ImageJ ?. 3D histogram texture features (mean, variance, skewness, kurtosis, and percentiles) were evaluated using MaZda software. Groupwise differences were investigated using Kruskal-Wallis tests and Mann-Whitney U tests. Gradient-boosted decision trees were created and trained to discriminate between control and cam-positive hips.

RESULTS:Both asymptomatic and symptomatic cam-FAI hips demonstrated significantly higher values of texture variance (p?=?0.0007, p?<?0.0001), 90th percentile (p?=?0.007, p?=?0.006), and 99th percentile (p?=?0.009, p?=?0.009), but significantly lower values of skewness (p?=?0.0001, p?=?0.0013) and kurtosis (p?=?0.0001, p?=?0.0001) compared to normal controls. There were no differences in texture profile between asymptomatic cam and symptomatic cam-FAI hips. Machine learning models demonstrated high classification accuracy for discriminating control hips from asymptomatic cam-positive (82%) and symptomatic cam-FAI (86%) hips.

CONCLUSIONS:Texture analysis can discriminate between normal and cam-positive hips using conventional descriptive statistics, regression modeling, and machine learning algorithms. It has the potential to become an important tool in compositional analysis of hip subchondral trabecular bone in the context of FAI, and possibly serve as a biomarker of joint degeneration.

KEY POINTS:? The CT texture profile of acetabular subchondral bone is significantly different between normal and cam-positive hips. ? Texture analysis can detect changes in subchondral bone in asymptomatic cam-positive hips that are equal to that of symptomatic cam-FAI hips. ? Texture analysis has the potential to become an important tool in compositional analysis of hip subchondral bone in the context of FAI and may serve as a biomarker in the study of joint physiology and biomechanics.

髋臼软骨下骨的CT纹理分析可以区分正常髋关节和凸轮阳性髋关节

目的：本研究的目的是确定髋臼软骨下骨的CT质地在正常髋关节、无症状的CAM阳性髋关节和有症状的CAM-FAI髋关节中是否有所不同。此外，利用机器学习的方法，探讨了纹理分析在区分三种髋关节状态中的作用。

方法：通过IRB批准的病例对照研究，分析2010年8月至2013年12月期间具有和不具有凸轮形态的受试者的CT图像。共68例受试者，其中正常对照组19例，无症状CAM 26例，有症状CAM-FAI 23例。采用ImageJ?在矢状位斜位CT图像上勾画髋臼软骨下骨轮廓。使用Mazda软件评估三维直方图纹理特征(均值、方差、偏度、峰度和百分位数)。采用Kruskal-Wallis检验和Mann-Whitney U检验分析GroupWise差异。梯度增强的决策树被创建和训练来区分对照髋关节和凸轮阳性髋关节。

结果：与正常对照组比较，无症状和有症状的CAM-FAI髋关节的质地方差(p?=?0.0007，p?<?0.0001)、第90个百分位数(p?=?0.007，p?=?0.006)和第99个百分位数(p?=?0.009，p?=?0.009)显著升高，而偏斜度(p?=?0.0001，p?=?0.0013)和峰度值(p?=?0.0001，p?=?0.0001)显著降低。无症状的CAM-FAI髋关节与有症状的CAM-FAI髋关节的质地分布无明显差异。机器学习模型显示出较高的分类准确率，可将对照髋关节与无症状的CAM阳性髋关节(82%)和有症状的CAM-FAI髋关节(86%)区分开来。

结论：使用传统的描述性统计、回归建模和机器学习算法，质地分析可以区分正常髋关节和凸轮阳性髋关节。它有可能成为FAI中髋关节软骨下松质骨成分分析的重要工具，并可能作为关节退变的生物标志物。

要点：·髋臼软骨下骨的CT质地轮廓在正常髋关节和凸轮阳性髋关节之间有显著差异。·质地分析可以检测出无症状的CAM阳性髋关节软骨下骨的变化，与有症状的CAM-FAI髋关节相同。纹理分析有可能成为FAI背景下髋关节软骨下骨成分分析的重要工具，可作为关节生理学和生物力学研究的生物标志物。