A Radiomic Model for Gliomas Grade and Patient Survival Prediction
Abstract
1. Introduction
- We simulate four predictive models to classify between LGGs and GBM images using a manual and/or automatic corrected segmentation mask.
- We evaluate four predictive models in the survival analysis for the patient with brain tumor using LGG and GBM MRI images.
- We assess the impact of LASSO and ICC features in the classification of LGGs and GBM and the survival analysis.
2. Method
Algorithm 1 Pipeline of radiomics model. | |
Require: Brain tumor MRI image I, segmentation masks and Ensure: Extracted features , trained models , performance metrics | |
| ▹ Segment image using mask ▹ Segment image using mask ▹ Extract features from the region of ▹ Extract features from the region of ▹ Apply ICC ▹ Apply LASSO regression ▹ Select stable and important features ▹ Train models using selected features ▹ Evaluate models using the test samples |
2.1. Dataset Description
2.2. Patients and Data Cleaning
2.3. Feature Extraction
2.4. Feature Selection
2.4.1. Interclass Correlation Coefficient-ICC
2.4.2. LASSO
2.5. Experimental Environment Details and Modeling
2.6. Performance Evaluation
2.7. Survival Analysis
3. Result
Ablation Study
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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GBM | |
---|---|
Number of patients | 97 |
Number of segments (lesions) | 194 |
Age (years) | |
Median (Range) | 56.5 (18–84) |
Gender | |
Male | 58 |
Female | 38 |
Unknown | 1 |
Os.time(days) | |
Median (Range) | 424.5 (5–2768) |
LGG | |
Number of patients | 62 |
Number of segments (lesions) | 124 |
Age (years) | |
Median (Range) | 43.5 (20–74) |
Gender | |
Male | 26 |
Female | 36 |
Os.time(days) | |
Median (Range) | 788 (3–4752) |
Feature (A/B) | Model | Accuracy | Sensitivity | Specificity | Precision | F-Score |
---|---|---|---|---|---|---|
LASSO features (290/286 features) | RF | 85.42/83.33 | 87.50/78.13 | 81.25/93.75 | 90.32/96.15 | 88.89/86.21 |
SVM | 89.58/87.50 | 93.75/90.63 | 81.25/81.25 | 90.91/90.63 | 88.52/82.76 | |
LR | 89.58/87.50 | 93.75/90.63 | 81.25/81.25 | 90.91/90.63 | 92.31/90.63 | |
XGB | 85.42/79.17 | 84.38/75.00 | 87.50/87.50 | 93.10/92.30 | 88.52/82.76 | |
ALL features (3860/3860 features) | RF | 91.67/85.42 | 87.50/81.25 | 100.00/93.75 | 100.00/96.30 | 93.33/88.14 |
SVM | 87.50/89.58 | 90.63/90.63 | 81.25/87.50 | 90.63/93.55 | 90.63/92.06 | |
LR | 87.50/89.58 | 90.63/90.63 | 81.25/87.50 | 86.67/92.86 | 90.63/93.55 | |
XGB | 91.67/83.33 | 93.75/75.00 | 87.50/100 | 93.75/100 | 93.75/85.71 | |
ICC features (1507/1507 features) | RF | 75.00/75.00 | 68.75/68.75 | 87.50/87.50 | 91.67/91.67 | 78.57/78.57 |
SVM | 77.08/83.33 | 71.88/90.63 | 87.50/68.75 | 92.00/85.29 | 80.70/87.88 | |
LR | 83.33/83.33 | 90.63/90.63 | 68.75/68.75 | 85.29/85.29 | 87.88/87.88 | |
XGB | 83.33/83.33 | 75.00/75.00 | 100.00/100.00 | 100.00/100.00 | 85.71/85.71 | |
Combined ICC and LASSO (1715/1709 features) | RF | 89.58/81.25 | 84.38/75.00 | 100.00/93.75 | 100.00/96.00 | 91.53/84.21 |
SVM | 85.42/85.42 | 84.38/78.13 | 87.50/100.00 | 93.10/100.00 | 88.52/87. 72 | |
LR | 85.42/85.42 | 84.38/78.13 | 87.50/100.00 | 93.10/100.00 | 88.52/87.72 | |
XGB | 89.58/83.33 | 90.63/78.13 | 87.50/93.75 | 93.55/96.15 | 92.06/86.21 |
Feature (A/B) | Model | Accuracy | Sensitivity | Specificity | Precision | F-Score |
---|---|---|---|---|---|---|
RF | 83.08 ± 7.23/81.13 ± 3.43 | 86.79 ± 7.98/89.79 ± 4.36 | 77.44 ± 9.42/67.82 ± 8.15 | 85.87 ± 5.46/81.52 ± 3.82 | 86.19 ± 5.95/85.32 ± 2.47 | |
LASSO features | SVM | 77.98 ± 4.43/77.98 ± 6.56 | 85.68 ± 5.71/86.68 ± 3.76 | 66.03 ± 4.01/64.36 ± 13.85 | 79.79 ± 1.92/79.64 ± 6.44 | 82.58 ± 3.48/82.90 ± 4.45 |
(290/286 features) | LR | 77.98 ± 1.99/83.63 ± 3.73 | 87.68 ± 3.85/88.68 ± 3.81 | 62.82 ± 8.67/76.03 ± 9.56 | 78.94 ± 3.25/85.47 ± 5.47 | 82.94 ± 1.12/86.88 ± 2.83 |
XGB | 83.71 ± 9.31/82.98 ± 6.84 | 88.84 ± 8.56/90.84 ± 7.48 | 75.77 ± 11.65/70.77 ± 8.76 | 85.23 ± 6.94/83.07 ± 4.51 | 86.95 ± 7.43/86.69 ± 5.30 | |
RF | 87.42 ± 1.98/86.79 ± 4.14 | 91.74 ± 2.57/91.74 ± 4.20 | 80.51 ± 6.95/78.85 ± 6.77 | 88.31 ± 2.86/87.35 ± 3.76 | 89.93 ± 1.30/89.45 ± 3.45 | |
ALL features | SVM | 85.52 ± 4.27/80.54 ± 4.89 | 88.57 ± 4.06/84.47 ± 4.84 | 80.51 ± 10.73/74.23 ± 9.05 | 88.27 ± 6.68/83.91 ± 5.19 | 88.22 ± 3.40/84.09 ± 4.11 |
(3860/3860 features) | LR | 84.90 ± 3.67/84.29 ± 2.71 | 86.47 ± 6.44/87.63 ± 2.49 | 82.18 ± 3.64/79.10 ± 3.52 | 88.39 ± 1.92/86.74 ± 2.47 | 87.31 ± 3.68/87.17 ± 2.31 |
XGB | 86.81 ± 2.24/88.69 ± 4.21 | 92.74 ± 2.67/91.74 ± 7.13 | 77.31 ± 6.46/83.85 ± 5.31 | 86.69 ± 2.77/89.92 ± 2.87 | 89.56 ± 1.76/90.66 ± 3.99 | |
RF | 86.77 ± 4.21/86.79 ± 3.05 | 91.68 ± 4.29/90.68 ± 6.16 | 79.10 ± 6.34/80.51 ± 4.53 | 87.26 ± 3.94/88.06 ± 1.79 | 89.37 ± 3.60/89.22 ± 3.07 | |
ICC features | SVM | 85.51 ± 3.90/84.88 ± 3.77 | 85.42 ± 7.12/92.68 ± 7.89 | 85.26 ± 6.51/72.56 ± 12.54 | 90.43 ± 3.49/84.80 ± 5.99 | 87.63 ± 3.80/88.12 ± 3.10 |
(1507/1507 features) | LR | 84.23 ± 4.62/86.11 ± 4.92 | 84.37 ± 7.59/87.53 ± 5.48 | 83.72 ± 5.49/83.59 ± 9.37 | 89.21 ± 3.28/89.73 ± 5.17 | 86.52 ± 4.41/88.47 ± 4.01 |
XGB | 88.04 ± 1.30/87.44 ± 3.37 | 93.79 ± 2.16/90.74 ± 7.64 | 78.97 ± 4.16/82.31 ± 5.94 | 87.55 ± 2.02/89.05 ± 3.17 | 90.53 ± 1.12/89.63 ± 3.42 | |
RF | 88.02 ± 3.75/87.42 ± 1.98 | 92.74 ± 2.67/91.74 ± 5.36 | 80.51 ± 8.72/80.26 ± 11.57 | 88.42 ± 4.30/88.76 ± 5.26 | 90.47 ± 2.83/89.93 ± 1.24 | |
Combined ICC and LASSO | SVM | 86.15 ± 2.58/85.52 ± 1.62 | 87.53 ± 5.48/89.63 ± 7.48 | 83.46 ± 11.88/78.59 ± 13.79 | 90.27 ± 6.22/87.93 ± 6.03 | 88.54 ± 2.02/88.27 ± 1.41 |
(1715/1709 features) | LR | 87.44 ± 3.37/86.79 ± 3.63 | 87.53 ± 7.96/88.58 ± 7.80 | 86.92 ± 4.40/83.59 ± 10.75 | 91.59 ± 1.77/90.17 ± 5.02 | 89.24 ± 3.73/88.98 ± 3.60 |
XGB | 86.17 ± 1.47/86.81 ± 3.58 | 91.79 ± 3.98/90.84 ± 6.62 | 77.44 ± 3.07/80.77 ± 7.83 | 86.45 ± 1.57/88.29 ± 3.08 | 88.97 ± 1.47/89.30 ± 3.08 |
Feature (A/B) | Model | p-Value | HR | CI | C-Index | Brier Score |
---|---|---|---|---|---|---|
RF | 0.018/0.007 | 2.21/2.48 | 1.13–4.35/1.26–4.88 | 0.689/0.690 | 0.111/0.121 | |
LASSO features | SVM | 0.002/0.189 | 3.56/1.60 | 1.54–8.27/0.79–3.23 | 0.609/0.567 | 0.142/0.185 |
(1417/3857 features) | LR | 0.062/0.099 | 1.88/1.76 | 0.96–3.71/0.89–3.46 | 0.636/0.623 | 0.211/0.144 |
XGB | 0.002/0.013 | 2.85/2.30 | 1.44–5.64/1.17–4.55 | 0.664/0.689 | 0.111/0.079 | |
RF | 0.012/0.005 | 2.33/2.57 | 1.18–4.59/1.30–5.06 | 0.681/0.718 | 0.108/0.119 | |
ALL features | SVM | 0.002/0.189 | 3.56/1.60 | 1.54–8.27/0.79–3.23 | 0.610/0.567 | 0.145/0.184 |
(3861/3861 features) | LR | 0.062/0.099 | 1.88/1.76 | 0.96–3.71/0.89–3.46 | 0.634/0.612 | 0.214/0.137 |
XGB | 0.037/0.013 | 2.08/2.30 | 1.03–4.18/1.17–4.55 | 0.652/0.689 | 0.131/0.080 | |
RF | 0.0003/0.0003 | 3.24/3.24 | 1.63–4.43/1.63–4.43 | 0.719/0.719 | 0.113/0.113 | |
ICC features | SVM | 0.011/0.017 | 2.53/2.32 | 1.20–5.30/1.14–4.72 | 0.592/0.592 | 0.200/0.189 |
(1508/1508 features) | LR | 0.002/0.002 | 2.85/2.85 | 1.44–5.64/1.44–5.64 | 0.695/0.695 | 0.173/0.173 |
XGB | 0.015/0.015 | 2.29/2.29 | 1.15–4.53/1.15–4.53 | 0.669/0.669 | 0.085/0.085 | |
RF | 0.002/0.003 | 2.83/2.67 | 1.43–5.59/1.35–5.26 | 0.701/0.711 | 0.105/0.119 | |
Combined ICC and LASSO | SVM | 0.002/0.189 | 3.56/1.60 | 1.54–8.27/0.79–3.23 | 0.610/0.567 | 0.143/0.181 |
(2537/3858 features) | LR | 0.062/0.140 | 1.88/1.66 | 0.96–3.71/0.84–3.27 | 0.634/0.623 | 0.212/0.164 |
XGB | 0.005/0.034 | 2.57/2.06 | 1.29–5.11/1.04–4.08 | 0.651/0.669 | 0.132/0.073 |
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Chaddad, A.; Jia, P.; Hu, Y.; Katib, Y.; Kateb, R.; Daqqaq, T.S. A Radiomic Model for Gliomas Grade and Patient Survival Prediction. Bioengineering 2025, 12, 450. https://doi.org/10.3390/bioengineering12050450
Chaddad A, Jia P, Hu Y, Katib Y, Kateb R, Daqqaq TS. A Radiomic Model for Gliomas Grade and Patient Survival Prediction. Bioengineering. 2025; 12(5):450. https://doi.org/10.3390/bioengineering12050450
Chicago/Turabian StyleChaddad, Ahmad, Pingyue Jia, Yan Hu, Yousef Katib, Reem Kateb, and Tareef Sahal Daqqaq. 2025. "A Radiomic Model for Gliomas Grade and Patient Survival Prediction" Bioengineering 12, no. 5: 450. https://doi.org/10.3390/bioengineering12050450
APA StyleChaddad, A., Jia, P., Hu, Y., Katib, Y., Kateb, R., & Daqqaq, T. S. (2025). A Radiomic Model for Gliomas Grade and Patient Survival Prediction. Bioengineering, 12(5), 450. https://doi.org/10.3390/bioengineering12050450