CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
- (1)
- The radiomics features extracted from retroperitoneal lymph nodes might potentially predict TGCT recurrence.
- (2)
- Integrating important clinical factors, including age, histotype, AFP, ß-HCG, and BMI, into a combined clinical-radiomics model might add an incremental value to predict TGCT recurrence.
2. Materials and Methods
2.1. Patients and Imaging Protocol
2.2. Segmentation and Radiomic Feature Extraction
2.3. Feature Selection and Development of the Predictive Radiomics Model
2.4. Development of the Clinical and the Combined Prediction Models
3. Results
3.1. Clinical Features
3.2. Feature Selection and Performance of the Radiomics Prediction Model
3.3. Performance of the Clinical and the Combined Prediction Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AFP | Alpha-fetoprotein |
AUC | Area under the curve |
CT | Computed tomography |
β-HCG | Beta human chorionic gonadotropin |
LNs | Lymph nodes |
LNM | Lymph node metastases |
LR | Logistic regression |
ML | Machine learning |
ROC | Receiver operating curve |
ROI | Region of interest |
TGCT | Testicular germ cell tumour |
Appendix A
Setting | Determination |
---|---|
Bin Method | FBN |
Bin Amount | 32 |
LoG Filter | 0 |
LoG Sigma | 2 |
Matrix Aggregation | 3D Average |
Method | Directions |
Resample Filter | 1 |
Resample Spacing X | 1 |
Resample Spacing Y | 1 |
Resample Spacing Z | 1 |
Second-Order Distance | 1 |
Threshold Filter | 0 |
Radiomics Features of First Order | Radiomics Features of Second Order: Gray Level Co-Occurrence Matrix (GLCM) |
---|---|
Histogram Minimum | Joint Maximum |
Histogram Maximum | Joint Average |
Histogram Range | Standart Deviation |
Histogram Mean | Joint Variance |
Histogram Variance | Joint Entropy |
Histogram Standart Deviation | Difference Average |
Histogram Skewness | Difference Variance |
Histogram Kurtosis | Difference Entropy |
Histogram Entropy | Sum of Averages |
Histogram Uniformity | Sum of Variance |
Histogram Mean Absolute Deviation | Sum of Entropy |
Histogram Robust Mean Absolute Deviation | Angular Second Moment |
Histogram Median Absolute Deviation | Contrast |
Histogram Coefficient Variation | Dissimilarity |
Histogram Quartile Coefficient Dispersion | Inverse Difference |
Histogram Interquartile Range | Inverse Difference Normalised |
Histogram P10th | Inverse Difference Moment |
Histogram P25th | Inverse Difference Moment Normalised |
Histogram P50th | Joint Maximum |
Histogram P75th | Joint Average |
Histogram P90th | Standart Deviation |
Histogram Minimum Histogram Gradient Intensity | Joint Variance |
Histogram MaximumHistogram Gradient Intensity | Joint Entropy |
Intensity Minimum | Difference Average |
Intensity Maximum | Difference Variance |
Intensity Range | Difference Entropy |
Intensity Mean | Sum of Averages |
Intensity Variance | Sum of Variance |
Intensity Standart Deviation | Sum of Entropy |
Intensity Skewness | Angular Second Moment |
Intensity Kurtosis | Contrast |
Intensity Energy | Dissimilarity |
Intensity P10th | Inverse Variance |
Intensity P25th | Correlation |
Intensity P50th | Auto Correlation |
Intensity P75th | Cluster Shade |
Intensity P90th | Cluster Prominence |
Intensity Root Mean Square | Cluster Tendency |
Intensity Mean Absolute Deviation | Information Correlation 1 |
Intensity Robust Mean Absolute Deviation | Information Correlation 2 |
Intensity Median Absolute Deviation | Inverse Variance 41 |
Intensity Coefficient Variation | |
Intensity Quartile Coefficient Dispersion | |
Intensity Interquartile Range 44 |
Radiomics Features of First Order | Radiomics Features of Second Order: Gray Level Co-Occurrence Matrix (GLCM) |
---|---|
Histogram Variance | Auto Correlation |
Intensity Maximum | Cluster Shade |
Intensity Mean | Cluster Prominence |
Intensity Variance | Cluster Tendency |
Intensity Energy | Contrast |
Intensity Root Mean Square | |
Intensity Median Absolute Deviation |
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Average age (range) | 35.2 ± 9.4 Years (18–63) |
Histological type | |
Seminoma | 60 Patients (66%) |
Non-seminoma | 31 Patients (34%) |
Tumour classification (T) | |
T1a | 64 (70%) |
T1b | 27 (30%) |
Tumour marker | |
AFP positive | 21 Patients (19%) |
Β-HCG positive | 40 Patients (44%) |
AFP und Β-HCG positive | 10 Patients (11%) |
BMI (range) | 25.9 ± 4.6 (19.3–43.9) |
Patients’ status in 6-year follow up | |
Complete remission (CR) | 81 (89%) |
Relapse of disease (RD) with metastatic lymph nodes | 10 (11%) |
Model | AUC (95% CI) | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|
Radiomics-only | 0.87± 0.04 | 0.80 ± 0.06 | 0.81 ± 0.06 | 0.80 ± 0.08 | 0.80 ± 0.06 |
Clinical-only | 0.75 ± 0.08 | 0.68 ± 0.10 | 0.66 ± 0.11 | 0.71 ± 0.16 | 0.68 ± 0.12 |
Combined clinical-radiomics | 0.89 ± 0.03 | 0.81 ± 0.04 | 0.80 ± 0.07 | 0.83 ± 0.06 | 0.81 ± 0.04 |
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Lisson, C.S.; Manoj, S.; Wolf, D.; Schrader, J.; Schmidt, S.A.; Beer, M.; Goetz, M.; Zengerling, F.; Lisson, C.G.S. CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer. Onco 2023, 3, 65-80. https://doi.org/10.3390/onco3020006
Lisson CS, Manoj S, Wolf D, Schrader J, Schmidt SA, Beer M, Goetz M, Zengerling F, Lisson CGS. CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer. Onco. 2023; 3(2):65-80. https://doi.org/10.3390/onco3020006
Chicago/Turabian StyleLisson, Catharina Silvia, Sabitha Manoj, Daniel Wolf, Jasper Schrader, Stefan Andreas Schmidt, Meinrad Beer, Michael Goetz, Friedemann Zengerling, and Christoph Gerhard Sebastian Lisson. 2023. "CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer" Onco 3, no. 2: 65-80. https://doi.org/10.3390/onco3020006