Suppr超能文献

CT 纹理分析髋臼软骨下骨可区分正常和凸轮阳性髋关节。

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

机构信息

Department of Radiology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, K1H 8M5, Canada.

Department of Medical Imaging, The Ottawa Hospital, 501 Smyth Road, Ottawa, K1H 8L6, Canada.

出版信息

Eur Radiol. 2020 Aug;30(8):4695-4704. doi: 10.1007/s00330-020-06781-1. Epub 2020 Apr 5.

Abstract

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.

摘要

目的

本研究旨在确定髋臼软骨下骨的 CT 纹理特征是否在正常、无症状凸轮阳性和有症状凸轮-FAI 髋关节之间存在差异。此外,还探讨了使用机器学习方法对纹理分析在区分三种髋关节状态方面的应用。

方法

本研究为回顾性病例对照研究,分析了 2010 年 8 月至 2013 年 12 月间具有和不具有凸轮形态的患者的 CT 图像。共纳入 68 例患者:19 例正常对照组,26 例无症状凸轮阳性组,23 例有症状凸轮-FAI 组。使用 ImageJ ® 在矢状斜 CT 图像上勾勒髋臼软骨下骨。使用 MaZda 软件评估 3D 直方图纹理特征(均值、方差、偏度、峰度和百分位数)。使用 Kruskal-Wallis 检验和 Mann-Whitney U 检验比较组间差异。创建并训练梯度提升决策树以区分对照组和凸轮阳性组。

结果

无症状凸轮阳性和有症状凸轮-FAI 组的纹理方差(p=0.0007,p<0.0001)、90 百分位数(p=0.007,p=0.006)和 99 百分位数(p=0.009,p=0.009)值显著升高,而偏度(p=0.0001,p=0.0013)和峰度(p=0.0001,p=0.0001)值显著降低。但无症状凸轮阳性组和有症状凸轮-FAI 组之间的纹理特征无差异。机器学习模型对区分正常对照组和无症状凸轮阳性(82%)和有症状凸轮-FAI(86%)组的髋关节具有较高的分类准确率。

结论

使用常规描述性统计、回归建模和机器学习算法,纹理分析可区分正常和凸轮阳性髋关节。它有可能成为 FAI 背景下髋关节软骨下骨成分分析的重要工具,并可能成为关节退变的生物标志物。

关键要点

  • 髋臼软骨下骨的 CT 纹理特征在正常和凸轮阳性髋关节之间存在显著差异。

  • 纹理分析可以检测到无症状凸轮阳性髋关节的软骨下骨变化,与有症状凸轮-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,并可能成为关节生理学和生物力学研究的生物标志物。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验