这篇文献2018年发表在European Radiology上： Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types. 这里的
AVM指的是 arteriovenous malformation.
1. feature extraction
(1) First-order statistics of hematoma intensity (n = 18),
(2) shape (n = 16),
(3) texture (n = 22, derived from GLCM),
(4) texture (n = 16, derived from GLRLM),
(5) wavelet-based features (n = 448),
(6) Laplacian of Gaussian-filtered image features (n = 56).
图像分割由两名放射科医生完成，作者将ICC(intraclass correlation coefficient) > 0.8 的特征筛选出来用于下一步的特征选择和建模。
2. feature selection
单变量分析（p < 0.1）
gini index (GINI),
information gain (IFGN),
gain ratio (GNRO),
Euclidean distance (EUDT),
t test-score (TSCR),
Wilcoxon rank sum (WLCR), and
fisher score (FSCR)
mutual information (MUIF) and
FS methods including GINI, RELF, IFGN, GNRO, and EUDT were performed by R software package “CORElearn” by the “attrEval” function.
FAOV and MUIF were conducted using the feature_selection module in sklearn (f_classif and mutual_info_classif), MRMR by the “pymrmr” package in Python.
We selected features according to rankings in their own group instead of rankings among all features since this enabled a systematic description of different aspects of the hematomas and avoided selecting features from a certain feature group.
3. machine learning and evaluation of the model
Eight supervised machine learning algorithms:
neural network (NN),
decision tree (Decision Tree),
Adaboost classifier (AD),
naïve Bayes (NB),
random forest (RF),
logistic regression (LG),
support vector machines (SVM), and
k nearest neighbors (KNN). ( through
sklearnpackage in Python)
这样，一共88（11*8）个models就建成了。研究者使用了threefold cross-validated对其进行训练，使用 AUC和RSD（relative standard deviation）来评价model的表现。其中，
RSD = (sdAUC/meanAUC) *100
The lower the RSD value, the more stable the predicting model.
Boxplot of ICC of features extracted from 6 feature groups
- Heatmaps illustrating the predictive performance (AUC) of different combinations of feature selection methods (rows) and classification algorithms (columns).
(a) Cross-validated AUC values of 88 models on the train and validation datasets.
(b) RSD values of 88 models on the train and validation datasets.
The model of RELF_Ada showed a best performance.
(a) Illustration of the threefold cross-validated ROC curve of model RELF_Ada.
(b) ROC curve of RELF_Ada on the test dataset.
(c) Confusion matrix with normalization of RELF_Ada
Comparison of prediction performance between the model and radiologists.