Radiomics and Its Feature Selection: A Review
Abstract
:1. Introduction
2. Radiomics Workflow
2.1. Image Acquisition and Pre-Processing
2.2. Image Segmentation
2.3. Feature Extraction and Feature Selection
- The Gray Level Size Zone Matrix (GLSZM) segments an image into regions with contiguous voxel values [103];
- The Neighboring Gray Tone Difference Matrix (NGTDM) quantifies the gray value of a voxel by considering the difference between its average gray value and the gray value within a certain distance of the neighborhood [104];
- The Gray Level Dependence Matrix (GLDM) calculates the difference between adjacent voxels based on their values [105].
2.4. Model Creation and Evaluation
2.5. Clinical Application
3. Feature Selection Method
3.1. The Feature Selection Framework
3.2. Classification of Feature Selection
- Distribution Analysis: The Mann–Whitney U-test measures the difference in the distribution of each feature within the positive and negative sample groups. The formula for is as follows:
- Decorrelation: The Pearson linear correlation coefficient calculates the correlation between each pair of features:
- Minimum Redundancy Maximum Relevance (mRMR): The mRMR method selects features that are distant from each other while still being highly correlated with the predicted labels. The method is based on mutual information, defined as follows:Assuming a total of X features, of them are selected to create the feature set . The m-th feature can be selected through a stepwise optimization process using the objective function.In the equation, y represents the classification variables of the samples in the training cohort, while and represent distinct features of the patients in the same training cohort.
- The Least Absolute Shrinkage and Selection Operator (LASSO) is a linear model that incorporates an -norm regularization to encourage sparse variable coefficients. It selects features with non-zero coefficients to form the final potential descriptor group for each specific task. The optimization objective for LASSO is represented as follows:Here, represents the feature vector of the n-th patient, is the classification variable, denotes the weight vector of the linear model, and is the normalization parameter [154].
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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First-order features | Shape-based features | Texture features | ||||
Gray Level Co-occurrence Matrix (GLCM) | Gray Level Size Zone Matrix (GLSZM) | Gray Level Dependence Matrix (GLDM) | Gray Level Run Length Matrix (GLRLM) | Neighboring Gray Tone Difference Matrix (NGTDM) | ||
Energy | Mesh Volume | Autocorrelation | Small Area Emphasis | Small Dependence Emphasis | Short Run Emphasis | Coarseness |
Total Energy | Voxel Volume | Joint Average | Large Area Emphasis | Large Dependence Emphasis | Long Run Emphasis | Contrast |
Entropy | Surface Area | Cluster Prominence | Gray Level Non-Uniformity | Gray Level Non-Uniformity | Gray Level Non-Uniformity | Busyness |
Minimum | Surface Area to Volume ratio | Cluster Shade | Gray Level Non-Uniformity Normalized | Dependence Non-Uniformity | Gray Level Non-Uniformity Normalized | Complexity |
10th percentile | Sphericity | Cluster Tendency | Size-Zone Non-Uniformity | Dependence Non-Uniformity Normalized | Run Length Non-Uniformity | Strength |
90th percentile | Maximum 3D diameter | Correlation | Size-Zone Non-Uniformity Normalized | Gray Level Variance | Run Length Non-Uniformity Normalized | |
Maximum | Maximum 2D diameter (Slice) | Difference Average | Zone Percentage | Dependence Variance | Run Percentage | |
Mean | Maximum 2D diameter (Column) | Difference Entropy | Zone Variance | Dependence Entropy | Gray Level Variance | |
Median | Maximum 2D diameter(Row) | Difference Variance | Zone Entropy | Low Gray Level Emphasis | Run Variance | |
Interquartile Range | Major Axis Length | Joint Energy | Low Gray Level Zone Emphasis | High Gray Level Emphasis | Run Entropy | |
Range | Minor Axis Length | Joint Entropy | High Gray Level Zone Emphasis | Small Dependence Low Gray Level Emphasis | Low Gray Level Run Emphasis | |
Mean Absolute Deviation | Least Axis Length | Informational Measure of Correlation | Small Area Low Gray Level Emphasis | Small Dependence High Gray Level Emphasis | High Gray Level Run Emphasis | |
Robust Mean Absolute Deviation | Elongation | Inverse Difference Moment | Small Area High Gray Level Emphasis | Large Dependence Low Gray Level Emphasis | Short Run Low Gray Level Emphasis | |
Root Mean Squared | Flatness | Maximal Correlation Coefficient | Large Area Low Gray Level Emphasis | Large Dependence High Gray Level Emphasis | Short Run High Gray Level Emphasis | |
Skewness | Inverse Difference Moment Normalized | Large Area High Gray Level Emphasis | Long Run Low Gray Level Emphasis | |||
Kurtosis | Inverse Difference | Long Run High Gray Level Emphasis | ||||
Variance | Inverse Difference Normalized | |||||
Uniformity | Invers Variance | |||||
Maximum Probability | ||||||
Sum Average | ||||||
Sum Entropy | ||||||
Sum of Squares |
Filter Methods | Selection Rules |
---|---|
Missing Percentage | Features with a disproportionate share of missing samples and difficult to fill were removed. |
Variance | Features with variance close to or equal to 0 were excluded. |
Frequency | Features that are excessively concentrated in one category of values are removed. |
Pearson Correlation Coefficient [129,130] | Features with correlation coefficients close to or equal to 0 were excluded. |
Spearman’s Rank Correlation Coefficient [131,132] | Features with correlation coefficients close to or equal to 0 were excluded. |
Kendall’s tau Rank Correlation Coefficient | Features with correlation coefficients close to or equal to 0 were excluded. |
Analysis of variance (ANOVA) [133,134] | Exclude features with too low an F-value, or exclude features with a p-value < 0.05. |
Chi-squared Test [135,136] | Features with too low a chi-squared value were excluded, or features with a p-value < 0.05 were excluded. |
Mutual Information [137,138] | Features with mutual information close to or equal to 0 were removed. |
mRMR [124] | The features with the minimum correlation and maximum redundancy were removed. |
Fisher Score [139] | Features with large intraclass distances and small interclass distances were excluded. |
Classification of Search Strategies (Subset Generation Process) | Algorithm Features | Subset Search Algorithm |
---|---|---|
Complete search | Iterate through all possible combinations of feature subsets, then select the feature subset with the best model score. | Breadth First Search [141] Best First Search [142] |
Heuristic search | The search is evaluated for each location searched, the best position is obtained, and the search is carried out from this position until the target is reached. | Sequential Forward Selection (SFS) [143,144] Sequential Backward Selection (SBS) [145] Bidirectional Search (BDS) [146] Plus-L Minus-R Selection (LRS) [147] Sequential Floating Selection (SFS) [148] Decision Tree Method (DTM) [149] |
Random search | A random subset of features is generated and then these feature subsets are given an evaluation. | Random Generation plus Sequential Selection (RGSS) [146] Simulated Annealing (SA) [150] Genetic Algorithms (GA) [151] |
Category | Advantage | Disadvantages |
---|---|---|
Filter | More efficient calculation Effectively avoid over-fitting Independent of the learning algorithms | Ignores interaction with the learning algorithms Weakeed learer fitting ability |
Wrapper | Simple Interacts with the learning algorithms Models feature dependencies | Risk of overfitting Learning algorithms-dependent selection A large number of calculations |
Embedded | Interacts with the learning algorithms Less complexity than Wrapper More efficient calculation | Learning algorithms-dependent selection Increases the model training burden |
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Zhang, W.; Guo, Y.; Jin, Q. Radiomics and Its Feature Selection: A Review. Symmetry 2023, 15, 1834. https://doi.org/10.3390/sym15101834
Zhang W, Guo Y, Jin Q. Radiomics and Its Feature Selection: A Review. Symmetry. 2023; 15(10):1834. https://doi.org/10.3390/sym15101834
Chicago/Turabian StyleZhang, Wenchao, Yu Guo, and Qiyu Jin. 2023. "Radiomics and Its Feature Selection: A Review" Symmetry 15, no. 10: 1834. https://doi.org/10.3390/sym15101834