Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients and PET/CT Imaging
2.2. Tumor Delination, Partial Volume Correction and Intensity Discretization
2.3. Matrix Construction with Different Aggregation Methods
- Features are computed from each 2D directional matrix and then averaged over 2D directions and slices, namely 2Daveraged;
- Features are computed from a single matrix after merging 2D directional matrices per slice, and then averaged over slices, namely 2Ds-meraged;
- Features are computed from a single matrix after merging 2D directional matrices per direction, and then averaged over directions, namely 2Dd-meraged;
- The feature is computed from a single matrix after merging all 2D directional matrices, namely 2Dmeraged;
- Features are computed from each 3D directional matrix and averaged over the 3D directions, namely 3Daveraged;
- The feature is computed from a single matrix after merging all 3D directional matrices, namely 3Dmeraged.
- Features are computed from 2D matrices and averaged over slices, namely 2D;
- The feature is computed from a single matrix after merging all 2D matrices, namely 2.5D;
- The feature is computed from a 3D matrix, namely 3D.
2.4. Feature Extraction
Characteristic | All Patients |
---|---|
Patient No. | 128 |
Age (year), mean SD | 47.7 ± 13.2 |
Sex, no.(%) | |
Male | 103 (80.5%) |
Female | 25 (19.5%) |
AJCC stage, no.(%) | |
I | 4 (3.1%) |
II | 11 (8.6%) |
III | 49 (38.3%) |
IV | 64 (50%) |
MATV | 50.9 ± 86.4 |
SUVmax | 15.4 ± 7.77 |
SUVmean | 7.95 ± 3.75 |
CTmean | 50.4 ± 55.1 |
2.5. Robustness Evaluation by Intra-Class Coefficient (ICC)
3. Results
3.1. Robustness of GLCM Features Affected by Aggregation Methods
3.2. Robustness of GLRLM Features Affected by Aggregation Methods
3.3. Robustness of GLSZM, GLDZM, NGLDM, NGTDM Features Affected by Aggregation Methods
3.4. Effect of Discretization and Partial Volume Correction on Feature’s Robustness
3.5. Effect of Discretization and Partial Volume Correction on Correlation between Features with Excellent Robustness and MATV
4. Discussion
4.1. The Research Value of This Study
4.2. The Effect of Different Aggregation Methods and Feature Dimension on the Feature’s Robustness
4.3. Statistical Metric and Cutoff Values
4.4. The Effect of Discretization and Partial Volume Corrections on the Percent of ICC Categories of All Texture Features
4.5. Robustness and Discriminability of Features
4.6. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Family | Count | Aggregation Methods |
---|---|---|
GLCM | 25 | 6 |
GLRLM | 16 | 6 |
GLSZM | 16 | 3 |
GLDZM | 16 | 3 |
NGTDM | 5 | 3 |
NGLDM | 17 | 3 |
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Peng, L.; Xu, H.; Lv, W.; Lu, L.; Chen, W. Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers 2023, 15, 932. https://doi.org/10.3390/cancers15030932
Peng L, Xu H, Lv W, Lu L, Chen W. Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers. 2023; 15(3):932. https://doi.org/10.3390/cancers15030932
Chicago/Turabian StylePeng, Lihong, Hui Xu, Wenbing Lv, Lijun Lu, and Wufan Chen. 2023. "Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT" Cancers 15, no. 3: 932. https://doi.org/10.3390/cancers15030932