Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Data Collection
2.2.1. Genetic Mutation Analysis
2.2.2. Imaging Data
2.3. Radiomics Analysis
2.3.1. Tumor Segmentation
2.3.2. Feature Extraction and Feature Selection
2.3.3. Data Pre-Processing and Modeling
2.4. External Validation
2.5. Statistics
3. Results
3.1. Study Population
3.2. Feature Selection and Modeling
3.3. Performance of the Models
3.3.1. Discrimination Accuracy
3.3.2. Calibration
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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Baseline Characteristics | Discovery Cohort (n = 255) | External Validation Set (n = 129) | p-Value |
---|---|---|---|
Age (years) | 62 (55–70) | 62 (53–69) | 0.607 |
Sex | 0.068 | ||
Male | 170 (66.7) | 73 (56.6) | |
Female | 85 (33.3) | 56 (43.4) | |
KRAS mutational status | 0.149 | ||
Mutation | 136 (53.3) | 58 (45.0) | |
Wild-type | 119 (46.7) | 71 (55.0) | |
Number of liver metastases | 12 (7–23) | 5 (2–11) | <0.0001 |
Machine-Learning Classifier | Cohort | AUC (95% CI) | Sensitivity | Specificity | Accuracy |
---|---|---|---|---|---|
Random Forest | Train | 0.97 (0.95–0.99) | 0.82 | 0.95 | 0.89 |
Test | 0.77 (0.62–0.93) | 0.85 | 0.75 | 0.80 | |
External validation | 0.54 (0.44–0.64) | 0.44 | 0.54 | 0.50 | |
Gradient Boosting | Train | 0.96 (0.94–1.00) | 0.95 | 0.98 | 0.98 |
Test | 0.77 (0.64–0.90) | 0.67 | 0.79 | 0.73 | |
External validation | 0.52 (0.42–0.62) | 0.46 | 0.57 | 0.52 | |
Gradient Boosting (LightGBM) | Train | 0.98 (0.97–1.00) | 0.98 | 0.99 | 0.98 |
Test | 0.72 (0.57–0.87) | 0.75 | 0.67 | 0.71 | |
External validation | 0.56 (0.46–0.67) | 0.28 | 0.59 | 0.42 | |
Ensemble | Train | 0.97 (0.94–1.00) | 0.97 | 0.93 | 0.95 |
Test | 0.86 (0.76–0.95) | 0.93 | 0.58 | 0.77 | |
External validation | 0.47 (0.37–0.56) | 0.30 | 0.74 | 0.50 |
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Wesdorp, N.; Zeeuw, M.; van der Meulen, D.; van ‘t Erve, I.; Bodalal, Z.; Roor, J.; van Waesberghe, J.H.; Moos, S.; van den Bergh, J.; Nota, I.; et al. Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models. Cancers 2023, 15, 5648. https://doi.org/10.3390/cancers15235648
Wesdorp N, Zeeuw M, van der Meulen D, van ‘t Erve I, Bodalal Z, Roor J, van Waesberghe JH, Moos S, van den Bergh J, Nota I, et al. Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models. Cancers. 2023; 15(23):5648. https://doi.org/10.3390/cancers15235648
Chicago/Turabian StyleWesdorp, Nina, Michiel Zeeuw, Delanie van der Meulen, Iris van ‘t Erve, Zuhir Bodalal, Joran Roor, Jan Hein van Waesberghe, Shira Moos, Janneke van den Bergh, Irene Nota, and et al. 2023. "Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models" Cancers 15, no. 23: 5648. https://doi.org/10.3390/cancers15235648
APA StyleWesdorp, N., Zeeuw, M., van der Meulen, D., van ‘t Erve, I., Bodalal, Z., Roor, J., van Waesberghe, J. H., Moos, S., van den Bergh, J., Nota, I., van Dieren, S., Stoker, J., Meijer, G., Swijnenburg, R. -J., Punt, C., Huiskens, J., Beets-Tan, R., Fijneman, R., Marquering, H., ... on behalf of the Dutch Colorectal Cancer Group Liver Expert Panel. (2023). Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models. Cancers, 15(23), 5648. https://doi.org/10.3390/cancers15235648