Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. Patients Characteristics
2.2. Features Selection and Survival Models Training
2.3. Models Validation and Comparison
2.4. Correlation between Radiomic Features and Clinical/Volumetric Variables
3. Discussion
4. Materials and Methods
4.1. Patients Population
4.2. Treatment
4.3. Follow-up
4.4. MRI Acquisition
4.5. Image Segmentation
4.6. Image Preprocessing
4.7. Radiomic Features Extraction
4.8. Survival Endpoints
4.9. Radiomic Features Postprocessing and Radiomic Model Development
4.10. Volume-Based Model Development
4.11. Clinical Model Development
4.12. Combined Model Development
4.13. Models Validation and Comparison
4.14. Correlation between Radiomic Features and Clinical/Volumetric Variables
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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PATIENTS CLINICAL DATA (N = 136) | |
---|---|
Age at diagnosis (years) 1 | 48 (39–57) |
Sex | Females: 41 (30%) Males: 95 (70%) |
T stage (VIII edition) | T2: 77 (57%) T3–T4: 59 (43%) |
N stage (VIII edition) | N1–N2: 69 (51%) N3: 67 (49%) |
Overall TNM stage (VIII edition) | I–III: 50 (37%) IV: 86 (63%) |
EBER positivity | Positive: 136 (100%) |
EBV-DNA load | Positive: 122 (90%) Negative: 14 (10%) |
Treatment | RT alone: 2 (1%) Concomitant CHT-RT: 43 (32%) Induction CHT + concomitant CHT-RT: 91 (67%) |
MRI ACQUISITION PARAMETERS | ||
---|---|---|
Image Type | T1-Weighted | T2-Weighted |
MRI scanner | Siemens Magnetom Avanto 1.5 T: 133 Others 1.5 T: 3 | |
Pulse sequence | Spin-echo | |
Echo train length 1 | 3 (3–3) | 13 (13–13) |
Number of averaging 1 | 2 (2–2) | 2 (2–2) |
Time of repetition (ms) 1 | 524 (477–588) | 4670 (3230–5300) |
Time of echo (ms) 1 | 12 (12–12) | 109 (107–109) |
Slice thickness (mm) 1 | 3 (3–3) | 3 (3–3) |
Slice spacing (mm) 1 | 3.9 (3.9–3.9) | 3.9 (3.9–3.9) |
Pixel spacing (mm) 1 | 0.57 (0.57–0.69) | 0.51 (0.49–0.57) |
Flip angle (°) 1 | 127 (127–127) | 134 (134–134) |
RF coil | Body |
RADIOMIC FEATURES STATISTICS | ||
---|---|---|
Feature | T-T1w-WaveletLLH-Firstorder-Median | T-T1w-WaveletLLL-Firstorder-Mean |
Mean (before/after normalization) | −0.015/0 | 2.006/0 |
Standard deviation (before/after normalization) | 0.014/1 | 0.737/1 |
Median (before/after normalization) | −0.013/0.151 | 1.940/−0.090 |
Interquartile range (before/after normalization) | 0.014/1.016 | 0.841/1.141 |
10th percentile (before/after normalization) | −0.032/−1.209 | 1.260/−1.012 |
90th percentile (before/after normalization) | −0.003/0.918 | 2.743/1.000 |
COX MODELS COEFFICIENT | ||||
---|---|---|---|---|
Feature Name | Radiomic Model | Clinical Model | Combined Model | Volume Model |
T-T1w-waveletLLH-firstorder-Median | 1.11 | - | 0.69 | - |
T-T1w-waveletLLL- firstorder-Mean | −0.75 | - | −0.45 | - |
Tumor volume 1 | - | - | - | 9.75 × 10−6 |
Age 2 | - | 0.07 | 0.05 | - |
Overall stage (VIII edition) | - | 1.48 | 1.27 | - |
Threshold for high risk | 0.29 | 4.29 | 3.23 | 0.16 |
Baseline Cumulative hazard (60 months) | 0.12 | 0.12 | 0.11 | 0.14 |
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Bologna, M.; Corino, V.; Calareso, G.; Tenconi, C.; Alfieri, S.; Iacovelli, N.A.; Cavallo, A.; Cavalieri, S.; Locati, L.; Bossi, P.; et al. Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers 2020, 12, 2958. https://doi.org/10.3390/cancers12102958
Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli NA, Cavallo A, Cavalieri S, Locati L, Bossi P, et al. Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers. 2020; 12(10):2958. https://doi.org/10.3390/cancers12102958
Chicago/Turabian StyleBologna, Marco, Valentina Corino, Giuseppina Calareso, Chiara Tenconi, Salvatore Alfieri, Nicola Alessandro Iacovelli, Anna Cavallo, Stefano Cavalieri, Laura Locati, Paolo Bossi, and et al. 2020. "Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients" Cancers 12, no. 10: 2958. https://doi.org/10.3390/cancers12102958