Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Adherence to Guidelines and Ethical Considerations
2.2. Patient Cohort Characteristics
2.3. CT Imaging Protocol
2.4. Image Segmentation
2.5. Feature Extraction and Analysis
2.6. Feature Stability and Selection
2.7. Machine Learning Models and Implementation
- Linear Models: Logistic Regression (LR), Support Vector Classifier (SVC), Quadratic Discriminant Analysis (QDA).
- Ensemble Methods: Random Forest Classifier (RFC), Extra Trees Classifier (ETC), Gradient Boosting Classifier (GBC), LightGBM Classifier (LGBM), CatBoost Classifier, and AdaBoost Classifier.
- Neural Networks: Multilayer Perceptron (MLPClassifier).
- Instance-Based Methods: K-Nearest Neighbours (KNN).
- Tree-Based Methods: Decision Tree Classifier (DTC).
2.8. Statistical Analysis
3. Results
3.1. Segmentation Results
3.2. Radiomic Feature Selection
3.3. Model Performance for Tumour Grade Prediction
3.4. Model Performance for Tumour Stage Prediction
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristic | Overall (n = 103) | Low Grade (n = 30) | High Grade (n = 73) | Early Stage (T1) (n = 58) | Advanced Stage (T2–T4) (n = 45) |
---|---|---|---|---|---|
Age, median (range) | 74 (49–93) | 67 (49–85) * | 76 (52–93) * | 72 (49–88) * | 77 (56–93) * |
Gender, n (%) | |||||
Male | 61 (59%) | 16 (53%) * | 45 (62%) * | 35 (60%) * | 26 (58%) * |
Female | 42 (41%) | 14 (47%) * | 28 (38%) * | 23 (40%) * | 19 (42%) * |
Smoking Status, n (%) | |||||
Current/Former | 80 (78%) | 20 (67%) * | 60 (82%) * | 44 (76%) * | 36 (80%) * |
Never | 23 (22%) | 10 (33%) * | 13 (18%) * | 14 (24%) * | 9 (20%) * |
BMI Category, n (%) | |||||
Normal (18.5–24.9) | 34 (33%) | 12 (40%) * | 22 (30%) * | 19 (33%) * | 15 (33%) * |
Overweight (25–29.9) | 35 (34%) | 10 (33%) * | 25 (34%) * | 20 (34%) * | 15 (33%) * |
Obese (≥30) | 34 (33%) | 8 (27%) * | 26 (36%) * | 19 (33%) * | 15 (33%) * |
Tumour Location, n (%) | |||||
Renal Pelvis | 49 (48%) | 20 (67%) * | 29 (40%) * | 28 (48%) * | 21 (47%) * |
Ureter | 54 (52%) | 10 (33%) * | 44 (60%) * | 30 (52%) * | 24 (53%) * |
Carcinoma in situ, n (%) | 25 (23%) | 4 (13%) * | 21 (29%) * | 13 (22%) * | 12 (27%) * |
Hydronephrosis, n (%) | 25 (23%) | 5 (17%) * | 20 (27%) * | 12 (21%) * | 13 (29%) * |
Multifocal, n (%) | 38 (35%) | 8 (27%) * | 30 (41%) * | 18 (31%) * | 20 (44%) * |
Tumour Size, mean ± SD (cm) | 1.97 ± 0.83 | 1.70 ± 0.70 * | 2.08 ± 0.86 * | 1.85 ± 0.73 * | 2.10 ± 0.90 * |
Deceased, n (%) | 58 (56%) | 8 (27%) * | 50 (68%) * | 28 (48%) * | 30 (67%) * |
Recurrence, n (%) | 31 (29%) | 5 (17%) * | 26 (36%) * | 14 (24%) * | 17 (38%) * |
Target | Data | Classifier | AUC Mean | AUC 95% CI | Sensitivity | Specificity | F1 Score |
---|---|---|---|---|---|---|---|
Grade | TUMOUR + 10 mm PRF | MLPClassifier | 0.961 | [0.920, 1.000] | 0.889 | 0.889 | 0.889 |
Tumour | RandomForestClassifier | 0.934 | [0.891, 0.977] | 0.867 | 0.867 | 0.863 | |
PRF 10 mm | CatBoostClassifier | 0.900 | [0.814, 0.986] | 0.783 | 0.884 | 0.841 | |
PRF 15 mm | MLPClassifier | 0.890 | [0.825, 0.956] | 0.806 | 0.806 | 0.802 | |
PRF 20 mm | LGBMClassifier | 0.883 | [0.825, 0.941] | 0.764 | 0.819 | 0.798 | |
PRF 25 mm | RandomForestClassifier | 0.876 | [0.816, 0.937] | 0.778 | 0.792 | 0.784 | |
PRF 30 mm | CatBoostClassifier | 0.874 | [0.813, 0.934] | 0.806 | 0.847 | 0.827 | |
STAGE | TUMOUR + 15 mm PRF | MLPClassifier | 0.852 | [0.790, 0.914] | 0.776 | 0.776 | 0.772 |
Tumour | MLPClassifier | 0.831 | [0.750, 0.911] | 0.780 | 0.746 | 0.765 | |
PRF 15 mm | LogisticRegression | 0.778 | [0.704, 0.851] | 0.702 | 0.667 | 0.682 | |
PRF 30 mm | ExtraTreesClassifier | 0.771 | [0.668, 0.874] | 0.638 | 0.690 | 0.669 | |
PRF 25 mm | AdaBoostClassifier | 0.759 | [0.639, 0.879] | 0.672 | 0.707 | 0.680 | |
PRF 10 mm | MLPClassifier | 0.756 | [0.657, 0.854] | 0.679 | 0.643 | 0.654 | |
PRF 20 mm | MLPClassifier | 0.711 | [0.641, 0.781] | 0.724 | 0.500 | 0.642 |
Model 1 | Model 2 | AUC 1 | AUC 2 | Z-Score | p-Value | |
---|---|---|---|---|---|---|
Grade | Tumour + 10 mm PRF MLPClassifier | Tumour Random Forest Classifier | 0.961 | 0.934 | 1.212807 | 0.225204 |
Tumour + 10 mm PRF MLPClassifier | PRF 10 mm Cat Boost Classifier | 0.961 | 0.9 | 2.416159 | 0.015685 | |
Tumour Random Forest Classifier | PRF 10 mm Cat Boost Classifier | 0.934 | 0.9 | 1.234756 | 0.216921 | |
Stage | Tumour + 15 mm PRF MLPClassifier | Tumour MLPClassifier | 0.852 | 0.831 | 0.575251 | 0.565122 |
Tumour + 15 mm PRF MLPClassifier | PRF 15 mm Logistic Regression | 0.852 | 0.778 | 1.914466 | 0.055561 | |
Tumour MLPClassifier | PRF 15 mm Logistic Regression | 0.831 | 0.778 | 1.339403 | 0.18044 |
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Al Mopti, A.; Alqahtani, A.; Alshehri, A.H.D.; Li, C.; Nabi, G. Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning. Cancers 2025, 17, 1220. https://doi.org/10.3390/cancers17071220
Al Mopti A, Alqahtani A, Alshehri AHD, Li C, Nabi G. Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning. Cancers. 2025; 17(7):1220. https://doi.org/10.3390/cancers17071220
Chicago/Turabian StyleAl Mopti, Abdulrahman, Abdulsalam Alqahtani, Ali H. D. Alshehri, Chunhui Li, and Ghulam Nabi. 2025. "Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning" Cancers 17, no. 7: 1220. https://doi.org/10.3390/cancers17071220
APA StyleAl Mopti, A., Alqahtani, A., Alshehri, A. H. D., Li, C., & Nabi, G. (2025). Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning. Cancers, 17(7), 1220. https://doi.org/10.3390/cancers17071220