Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.
Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.
复制标题
肺结节的放射素学：鲁棒性和定量成像特征一致性的多机构研究。
DOI：
10.18383/j.tom.2016.00235
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Goldgof D
中科院分区：
文献类型：
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作者：
Kalpathy-Cramer J;Mamomov A;Zhao B;Lu L;Cherezov D;Napel S;Echegaray S;Rubin D;McNitt-Gray M;Lo P;Sieren JC;Uthoff J;Dilger SK;Driscoll B;Yeung I;Hadjiiski L;Cha K;Balagurunathan Y;Gillies R;Goldgof D
放射组学是在放射学和肿瘤学的分类和预测任务中提供正常和异常组织的定量描述符。定量成像网络成员正在开发放射“特征”集，以概括肿瘤的大小、形状、纹理、强度、边缘和其他方面的结节和病变的成像特征。目前正在努力开发一个本体论来描述肺结节的放射学特征，主要类别包括大小、局部和全局形状描述符、边缘、强度和基于纹理的特征，这些特征基于小波、高斯拉普拉斯、劳特征、灰度共生矩阵和游程特征。这项研究的目的是调查肺结节的定量描述符对分割的敏感性，并说明不同特征类型和通过不同特征提取算法计算的特征之间的比较。我们计算了这些特征的一致性相关系数，以衡量它们与底层分割的稳定性；在本研究的830个特征中，68%的一致性CC为≥0.75%。特征对之间的成对相关系数被用来揭示特征之间的关联，特别是由不同参与者测量的关联。在给定的相关阈值下，使用图形模型方法来计数不相关特征组的数量。在0.75和0.95的阈值下，分别有75和246个子组，提供了特征冗余的测量。
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.