Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Data Sets
2.2. MR Imaging and Pre-Processing
2.3. Development of the Models
2.3.1. Deep-Learning Network
2.3.2. Radiomics Model Development
2.4. Methods Performance Comparison
3. Results
3.1. Internal Cross-Validation
3.2. External-Validation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Radiomics Features Extraction
Appendix B. Adaptive Workflow Optimization for Automatic Decision-Model Creation
References
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Training Cohort | Testing Cohort | |||
---|---|---|---|---|
Patient Cohort | Active Surveillance | Prodrome | ProstateX * | PCMM |
Total Number of patients | 271 | 100 | 195 | 78 |
Patients with a lesion ISUP grade = 1 | 155 | 68 | 128 | 28 |
Patients with a lesion ISUP grade ≥ 2 | 116 | 32 | 67 | 50 |
Total number of lesions | 233 | 104 | 328 | 156 |
ISUP grade 1 | 100 | 52 | 254 | 77 |
ISUP grade ≥ 2 | 133 | 52 | 74 | 79 |
ISUP grade 2 | 124 | 45 | NA | 68 |
ISUP grade 3 | 3 | 6 | NA | 8 |
ISUP grade 4 & 5 | 6 | 1 | NA | 3 |
Lesions in PZ | 150 | 60 | 191 | 104 |
Lesions in TZ | 33 | 41 | 82 | 49 |
Lesions in other zones (central, anterior stroma) | 38 | 3 | 55 | 3 |
Lesion volume (mL) | 0.3(0.2–0.8) | 0.61 (0.3–1.0) | 1.42 (1.4–3.2) | 0.80 (0.2–1.1) |
Prostate Volume(mL) | 43.1 (30.5–76.2) | 50. (33–67) | NA | NA |
Age (year) | 67 ± 7 | 68 ± 4 | NA | NA |
PSA(mean ± std ng/mL) | 10 ± 6 | 12 ± 4 | NA | 9 ± 7 |
Active Surveillance | Prodrome | ProstateX | PCMM | |||||
---|---|---|---|---|---|---|---|---|
Metrics | DL | Radiomics | DL | Radiomics | DL | Radiomics | DL | Radiomics |
AUC | 0.89 | 0.83 | 0.70 | 0.88 | 0.73 | 0.91 | 0.44 | 0.65 |
Accuracy | 0.76 | 0.63 | 0.58 | 0.78 | 0.71 | 0.85 | 0.52 | 0.55 |
Sensitivity | 0.85 | 1.00 | 0.72 | 1.00 | 0.70 | 0.72 | 0.70 | 0.44 |
Specificity | 0.52 | 0.54 | 0.51 | 0.68 | 0.71 | 0.94 | 0.18 | 0.71 |
F1-score | 0.74 | 0.66 | 0.52 | 0.78 | 0.65 | 0.85 | 0.66 | 0.55 |
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Castillo T., J.M.; Arif, M.; Starmans, M.P.A.; Niessen, W.J.; Bangma, C.H.; Schoots, I.G.; Veenland, J.F. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers 2022, 14, 12. https://doi.org/10.3390/cancers14010012
Castillo T. JM, Arif M, Starmans MPA, Niessen WJ, Bangma CH, Schoots IG, Veenland JF. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers. 2022; 14(1):12. https://doi.org/10.3390/cancers14010012
Chicago/Turabian StyleCastillo T., Jose M., Muhammad Arif, Martijn P. A. Starmans, Wiro J. Niessen, Chris H. Bangma, Ivo G. Schoots, and Jifke F. Veenland. 2022. "Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics" Cancers 14, no. 1: 12. https://doi.org/10.3390/cancers14010012
APA StyleCastillo T., J. M., Arif, M., Starmans, M. P. A., Niessen, W. J., Bangma, C. H., Schoots, I. G., & Veenland, J. F. (2022). Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers, 14(1), 12. https://doi.org/10.3390/cancers14010012