Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.1.1. Patient Cohort
2.1.2. SBRT Details
2.2. Image Acquisition
2.3. Image Segmentation
2.4. Outcome
2.5. Radiomics Analysis
2.5.1. Feature Extraction
2.5.2. Harmonization Process
2.5.3. Feature Selection
2.5.4. Model Building
2.5.5. Statistical Analysis
3. Results
3.1. Clinical Results
3.2. PFS Models
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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CT | |||||||
---|---|---|---|---|---|---|---|
Center | kV | mAs (Min–Max) | Slice Thickness (mm) | Manufacturer (s) | Convolution Kernel | Recon Diameter | |
TRAIN | BS | 120 | 191–401 | 3.0 | PHILIPS | B | 500 |
RE | 120 | 83–355 | 3.0 | GE | STD + | 500 | |
PD | 120 | 70–363 | 2.5 | GE | BODY FILTER | 500 | |
EXT VAL | AV | 120 | 108–138 | 2.5 | PHILIPS | B | 500 |
NE | 120 | 40–73 | 3.0 | SIEMENS | B30f | 500 | |
PI | 120 | 27–236 | 2.0 | SIEMENS | B30f–B31s | 500 | |
PG | 120 | 80–200 | 2.5–3 | GE | STD + | 500 | |
PET | |||||||
Center | Slice thickness (mm) | Manufacturer (s) | Recon diameter | Recon method | |||
TRAIN | BS | 3.27 | GE | 700–815 | 3D IR/VPFXS | ||
RE | 3.27 | GE | 700–700 | 3D IR/VPFXS | |||
PD | 2–4 | PHILIPS|SIEMENS | 576–815 | 3D-RAMLA/BLOB-OS-TF(PHILIPS)|PSF 3i21s/(SIEMENS) | |||
EXT VAL | AV | 4 | PHILIPS|GE | 500–700 | BLOB-OS-TF/VPFXS | ||
NE | 2–5 | SIEMENS | 576–700 | PSF+TOF 3i21s | |||
PI | 3.27 | GE|PHILIPS | 576–700 | 3D IR (GE)|BLOB-OS-TF(PHILIPS) | |||
PG | 3.27 | GE|SIEMENS | 600–700 | OSEM|OSEM 2i8s |
Characteristics | Training Cohort (N = 76) | External Validation Co#Hort (N = 41) | p |
---|---|---|---|
Gender | |||
Male | 61 | 24 | 0.04 |
Female | 15 | 17 | |
Age (years) | 78 [51–87] | 79 [57–88] | 0.72 |
Smoking Status | |||
Yes | 50 | 27 | 0.22 |
No | 26 | 14 | |
Performance Status | |||
0 | 37 | 18 | 0.75 |
1 | 35 | 15 | |
2 | 4 | 7 | |
BMI | 25.2 [16.4–37.1] | 24.8 [18.3–44.7] | 0.17 |
Diabetes mellitus | |||
Yes | 16 | 12 | 0.58 |
No | 60 | 29 | |
BPCO | |||
Yes | 43 | 17 | 0.54 |
No | 19 | 24 | |
Charlson Comorbidity Index (CCI) | |||
Median | 6.5 | 6 | 0.55 |
Range | [3–13] | [4–10] | |
T diameter | |||
Median | 2.35 | 2.3 | 0.58 |
Range | [0.6–5.5] | [0.72–27] | |
Lesion type | |||
Subsolid | 5 | 4 | 0.42 |
Solid | 71 | 37 | |
Lung Side | |||
Lung right | 42 | 22 | 0.006 |
Lung left | 34 | 19 | |
Lobe Site | |||
Upper Lobe | 44 | 23 | 0.89 |
Lower Lobe | 30 | 15 | |
Middle Lobe | 2 | 1 | |
Lesion Site | |||
Peripheral | 55 | 34 | 0.92 |
Central | 21 | 7 | |
BED10 | |||
Median | 115.5 | 100 | 0.64 |
Range | [100–180] | [100–132] |
Harmo CT + Original PET Features (A) | |||||
---|---|---|---|---|---|
Linear SVM (A1) | |||||
AUC * | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.77 [0.66–0.87] | 0.72 ± 0.02 | 0.67 | 0.83 | 1.0 × 10–4 |
External validation dataset | 0.75 [0.55–0.88] | 0.66 ± 0.01 | 0.68 | 0.65 | 0.01 |
Subspace Discriminant (A2) | |||||
AUC * | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.79 [0.67–0.87] | 0.71 ± 0.01 | 0.69 | 0.83 | 0.02 |
External validation dataset | 0.71 [0.52–0.86] | 0.63 ± 0.02 | 0.68 | 0.65 | 0.046 |
Harmo CT features (B) | |||||
Linear SVM (B1) | |||||
AUC | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.77 [0.63–0.85] | 0.67 ± 0.02 | 0.74 | 0.58 | 1.0 × 10−4 |
External validation dataset | 0.56 [0.39–0.74] | 0.58 ± 0.01 | 0.67 | 0.52 | 0.5 |
Subspace Discriminant (B2) | |||||
AUC | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.76 [0.66–0.87] | 0.71 ± 0.02 | 0.73 | 0.6 | 0.01 |
External validation dataset | 0.57 [0.4–0.75] | 0.58 ± 0.01 | 0.67 | 0.52 | 0.50 |
Original CT features (C) | |||||
Linear SVM (C1) | |||||
AUC | Accuracy | Precision ** | Recall ** | ||
Training dataset | 0.56 [0.42–0.68] | 0.52 ± 0.03 | 0.49 | 0.45 | |
External validation dataset | 0.50 [0.34–0.68] | 0.43 ± 0.02 | 0.54 | 0.65 | |
Subspace Discriminant (C2) | |||||
AUC | Accuracy | Precision ** | Recall ** | ||
Training dataset | 0.63 [0.48–0.72] | 0.56 ± 0.03 | 0.58 | 0.56 | |
External validation dataset | 0.51 [0.39–0.74] | 0.54 ± 0.01 | 0.58 | 0.65 | |
PET features only (D) | |||||
Linear SVM (D1) | |||||
AUC | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.68 [0.53-0.78] | 0.64 ± 0.03 | 0.64 | 0.80 | 0.09 |
External validation dataset | 0.65 [0.43-0.82] | 0.64 ± 0.01 | 0.67 | 0.78 | 0.18 |
Subspace Discriminant (D2) | |||||
AUC | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.71 [0.59–0.82] | 0.69 ± 0.01 | 0.67 | 0.8 | 0.10 |
External validation dataset | 0.68 [0.51–0.84] | 0.60 ± 0.01 | 0.67 | 0.61 | 0.08 |
Harmo CT + Original PET + Clinical features (E) | |||||
Linear SVM (E1) | |||||
AUC * | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.79 [0.67–0.87] | 0.73 ± 0.02 | 0.72 | 0.83 | 6.0 × 10−5 |
External validation dataset | 0.73 [0.54–0.87] | 0.73 ± 0.01 | 0.77 | 0.74 | 0.02 |
Subspace Discriminant (E2) | |||||
AUC * | Accuracy | Precision ** | Recall ** | p *** | |
Training dataset | 0.76 [0.65–0.86] | 0.74 ± 0.01 | 0.72 | 0.83 | 0.01 |
External validation dataset | 0.75 [0.54–0.88] | 0.68 ± 0.02 | 0.73 | 0.70 | 0.02 |
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Bertolini, M.; Trojani, V.; Botti, A.; Cucurachi, N.; Galaverni, M.; Cozzi, S.; Borghetti, P.; La Mattina, S.; Pastorello, E.; Avanzo, M.; et al. Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer. Curr. Oncol. 2022, 29, 5179-5194. https://doi.org/10.3390/curroncol29080410
Bertolini M, Trojani V, Botti A, Cucurachi N, Galaverni M, Cozzi S, Borghetti P, La Mattina S, Pastorello E, Avanzo M, et al. Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer. Current Oncology. 2022; 29(8):5179-5194. https://doi.org/10.3390/curroncol29080410
Chicago/Turabian StyleBertolini, Marco, Valeria Trojani, Andrea Botti, Noemi Cucurachi, Marco Galaverni, Salvatore Cozzi, Paolo Borghetti, Salvatore La Mattina, Edoardo Pastorello, Michele Avanzo, and et al. 2022. "Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer" Current Oncology 29, no. 8: 5179-5194. https://doi.org/10.3390/curroncol29080410
APA StyleBertolini, M., Trojani, V., Botti, A., Cucurachi, N., Galaverni, M., Cozzi, S., Borghetti, P., La Mattina, S., Pastorello, E., Avanzo, M., Revelant, A., Sepulcri, M., Paronetto, C., Ursino, S., Malfatti, G., Giaj-Levra, N., Falcinelli, L., Iotti, C., Iori, M., & Ciammella, P. (2022). Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer. Current Oncology, 29(8), 5179-5194. https://doi.org/10.3390/curroncol29080410