膝关节骨关节炎受试者膝关节X线片最小后处理后的骨密度和质地
Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis
摘要：
X线平片是评估骨关节炎分期的最常用方法。我们的目的是评估最小和临床后处理的X线片之间基于X线片的骨密度和纹理的关系，并比较对照组和膝关节骨关节炎或胫骨内侧骨髓病变（BML）受试者之间骨特征的差异。胫骨骨密度和纹理进行了评价，从射线照片与最低限度和临床后处理在109名受试者与非骨关节炎。使用分形特征分析评价骨纹理。在所有区域（0.94和0.97之间），在最小和临床后处理的X线照片之间的校准骨密度均存在显著相关性（p<0.001）。FD_Ver纹理变量的相关性在0.51和0.97之间变化（p<0.001），FD_Hor在-0.10和0.97之间变化。骨密度和质地在对照组和患有骨关节炎或BML的受试者之间存在差异（p<0.05），主要在胫骨内侧区域。当使用具有骨骼特征的基于机器学习的弹性网络模型对健康和骨关节炎受试者进行分类时，受试者工作特征（ROCAUC）曲线下面积为0.77。对于对照组和BML受试者的分类，ROCAUC为0.85。总之，当使用最小后处理时，可以从膝关节X线片评估骨密度和纹理的差异。
英文摘要：
Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p<0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p<0.001) for FD_Ver texture variable and between -0.10 and 0.97 for FD_Hor. Bone density and texture were different (p<0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
