The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning †
Abstract
:1. Introduction
- Sphericity, 3D maximum diameter, and surface area are three significant shape features relevant to the survival analysis of glioblastoma patients. Furthermore, these features from the enhancing tumor have more effect on patients’ survival time than those from the whole tumor based on hazard ratio.
- Aside from shape features, the extracted features (known as deep features) from enhancing tumor and whole tumor are essential for survival prediction compared to those from the whole tumor only; therefore, enhancing tumor’s characteristics affects the survival prediction for GBM patients.
2. Related Work
3. Materials and Methods
3.1. Overview
3.2. Dataset
3.3. Segmentation
3.4. Tumor Shape Radiomics Features Analysis
- is the time point at least one patient dead.
- is number of patients dead at .
- is number of patients surviving until time .
- h(t|z): hazard function determined by given vector of several predictors z.
- : is an unspecific function of time.
- : hazard ratio (HR). HR = 1: predictors does not have effect on survival time, HR < 1: associated with improved survival time, HR > 1: associated with increased risk or decreased survival time [21].
3.5. Deep Feature Extraction
3.6. Overall Survival Prediction
4. Results
4.1. Segmentation
4.2. Evaluation Metrics for Survival Prediction
4.3. Shape Radiomics Features Analysis
HR | 95% CI | p-Value | |
---|---|---|---|
Enhancing Tumor | |||
Elongation | 0.779 | 0.284–2.138 | 0.6 |
Flatness | 0.837 | 0.319–2.195 | 0.7 |
Least Axis Length | 1.018 | 1.002–1.035 | 0.06 |
Major Axis Length | 1.013 | 1.004–1.021 | 0.07 |
Maximum 2D Diameter Column | 1.011 | 0.960–1.022 | 0.06 |
Maximum 2D Diameter Row | 1.014 | 1.000–1.024 | 0.004 |
Maximum 2D Diameter Slice | 1.012 | 0.990–1.021 | 0.005 |
Maximum 3D Diameter | 1.112 | 1.023–1.223 | 0.005 |
Mesh Volume | 1 | 1–1 | 0.05 |
Minor Axis Length | 1.019 | 1.004–1.033 | 0.06 |
Sphericity | 0.184 | 0.049–0.698 | 0.01 |
Surface Area | 1.231 | 1.139–1.342 | 0.0001 |
Surface Volume Ratio | 1.054 | 0.642–1.729 | 0.8 |
Voxel Volume | 1 | 1–1 | 0.04 |
Whole Tumor | |||
Elongation | 0.514 | 0.122–2.160 | 0.4 |
Flatness | 0.315 | 0.068–1.456 | 0.1 |
Least Axis Length | 1.012 | 0.995–1.028 | 0.2 |
Major Axis Length | 1.012 | 1.003–1.012 | 0.07 |
Maximum 2D Diameter Column | 1.008 | 1.000–1.016 | 0.06 |
Maximum 2D Diameter Row | 1.009 | 0.992–1.017 | 0.04 |
Maximum 2D Diameter Slice | 1.010 | 1.000–1.017 | 0.03 |
Maximum 3D Diameter | 1.017 | 1.011–1.028 | 0.03 |
Mesh Volume | 1 | 1–1 | 0.1 |
Minor Axis Length | 1.016 | 0.984–1.031 | 0.02 |
Sphericity | 0.2669 | 0.069–0.762 | 0.02 |
Surface Area | 1.023 | 1.003–1.127 | 0.02 |
Surface Volume Ratio | 0.796 | 0.201–3.151 | 0.7 |
Voxel Volume | 1 | 1–1 | 0.1 |
Clinical Information | |||
Age | 1.036 | 1.021–1.051 | 0.000001 |
4.4. Overall Survival Prediction
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Dice ET | Dice TC | Dice WT | HD 95 ET | HD 95 TC | HD 95 WT |
---|---|---|---|---|---|---|
DKNet | 0.8182 | 0.8181 | 0.8876 | 2.6835 | 5.6112 | 4.6000 |
DFs | Accuracy | MSE |
---|---|---|
WT DFs | 0.393 | 244,139.9 |
ET DFs | 0.321 | 185,454.8 |
Selected DFs (18 features) | 0.464 | 123,320.8 |
Features | Linear Regression | LightGBM | MLP | |||
---|---|---|---|---|---|---|
Acc | MSE | Acc | MSE | Acc | MSE | |
SFs + Age | 0.536 | 128,839.4 | 0.500 | 108,577.9 | 0.536 | 99,482.7 |
SFs + DFs + Age | 0.500 | 116,463.9 | 0.464 | 99,577.4 | 0.571 | 97,531.8 |
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Trinh, D.-L.; Kim, S.-H.; Yang, H.-J.; Lee, G.-S. The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning. Electronics 2022, 11, 1038. https://doi.org/10.3390/electronics11071038
Trinh D-L, Kim S-H, Yang H-J, Lee G-S. The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning. Electronics. 2022; 11(7):1038. https://doi.org/10.3390/electronics11071038
Chicago/Turabian StyleTrinh, Dang-Linh, Soo-Hyung Kim, Hyung-Jeong Yang, and Guee-Sang Lee. 2022. "The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning" Electronics 11, no. 7: 1038. https://doi.org/10.3390/electronics11071038
APA StyleTrinh, D.-L., Kim, S.-H., Yang, H.-J., & Lee, G.-S. (2022). The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning. Electronics, 11(7), 1038. https://doi.org/10.3390/electronics11071038