Peng Lihong, Xu Hui, Lv Wenbing, Lu Lijun, Chen Wufan
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.
Cancers (Basel). 2023 Feb 1;15(3):932. doi: 10.3390/cancers15030932.
This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on F-FDG PET/CT images.
128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC.
There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods.
Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
本研究旨在探讨用于生成纹理特征的聚合方法对基于F-FDG PET/CT图像的鼻咽癌（NPC）纹理特征稳健性的影响。
纳入128例NPC患者，为每位患者提取95个纹理特征，包括不同聚合方法下的六个特征族。对于灰度共生矩阵（GLCM）和灰度行程长度矩阵（GLRLM）特征，考虑了六种聚合方法。对于灰度大小区域矩阵（GLSZM）、灰度差区域矩阵（GLDZM）、邻域灰度差矩阵（NGTDM）和邻域灰度依赖矩阵（NGLDM）特征，考虑了三种聚合方法。通过两两组内相关系数（ICC）评估受聚合方法影响的特征的稳健性。此外，通过总体ICC而非两两ICC评估离散化和部分容积校正（PVC）对所有纹理特征ICC类别百分比的影响。
有12个特征在不同聚合方法下具有出色的两两ICC，即联合平均、和平均、自相关、长行程强调、高灰度级行程强调、短行程高灰度级强调、长行程高灰度级强调、行程长度方差、SZM高灰度级强调、DZM高灰度级强调、高灰度级计数强调和依赖计数百分比。对于GLCM和GLRLM特征，19/25和14/16个特征在相同维度特征（2D、2.5D或3D）上，在不同聚合方法（平均和合并）下显示出出色的两两ICC。不同的离散化水平和部分容积校正导致受聚合方法影响的纹理特征具有一致的稳健性。
与采用不同聚合方法的相同维度特征相比，采用相同聚合方法的不同维度特征显示出较差的稳健性。不同的离散化水平和PVC算法对所有纹理特征的ICC类别百分比影响可忽略不计。
