Then, the median absolute deviations (MAD) was calculated for each remained feature21. Features with MAD equal to zero were discarded, as these features were considered as non-informative. After this step, 33881 features were left. Next, we further selected features with prognostic value. Here the prognostic performance is assessed using the concordance index (C-index), a generalization of the area under the receiver operating characteristic (ROC) curve (AUC)22. The C-index for each feature was calculated. Features with C-index ≥ 0.580 are considered as predictive factors. After prognostic performance analysis, 1581 features remained. Then, we further reduced the data dimension by removing highly correlated features. Here the correlation coefficient between each pair of features is calculated. For feature pair with correlated coefficient ≥0.90, the more prognostic feature is retained and the other feature is removed. Finally, the remained 150 image features are selected and regarded as robust, predictive and nonredundant. 解释

该段文字描述了一个数据特征选择的过程。首先，对于所有特征，计算其中位数绝对偏差（MAD），并移除MAD等于零的特征，因为这些特征被认为是非信息性的。经过此步骤，剩下33881个特征。然后，使用协调指数（C-index）对这些特征进行预测价值分析，C-index是接收者操作特征（ROC）曲线下面积（AUC）的推广。具有C-index≥0.580的特征被视为预测性因素。经过预测性能分析后，剩下1581个特征。接下来，通过计算特征之间的相关系数，进一步减少数据维度。对于相关系数≥0.90的特征对，保留更具预测性的特征，移除另一个特征。最后，剩下150个图像特征被选为具有稳健性、预测性和非冗余性的特征。