Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Study Population
2.2. NGS
2.3. CT Image Acquisition
2.4. Lesion Segmentation and Extraction of Radiomic Features
2.5. Selection of Radiomic Features
2.6. Model Development
2.7. Statistical Analysis
3. Results
3.1. Demographics
3.2. Extraction and Selection of Radiomic Features
3.3. Model Performance
4. Discussion
5. Conclusion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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True Label | Radiomic Features | Combined Model (Radiomic Features and Clinical Factors) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wildtype | Mutation | Sensitivity | Specificity | Accuracy | AUC | Wildtype | Mutation | Sensitivity | Specificity | Accuracy | AUC | ||
EGFR | Wildtype (n = 69) | 57 | 12 | 0.63(0.50–0.74) | 0.83(0.71–0.90) | 0.73(0.65–0.80) | 0.77(0.70–0.85) | 66 | 3 | 0.52(0.40–0.65) | 0.96(0.87–0.99) | 0.75(0.66–0.82) | 0.78(0.70–0.86) |
Mutation (n = 65) | 24 | 41 | 31 | 34 | |||||||||
KRAS | Wildtype (n = 119) | 49 | 70 | 0.93(0.66–0.99) | 0.41(0.32–0.51) | 0.47(0.38–0.56) | 0.70(0.57–0.83) | 81 | 38 | 0.87(0.58–0.97) | 0.68(0.59–0.76) | 0.70(0.62- 0.78) | 0.81(0.69–0.93) |
Mutation (n = 15) | 1 | 14 | 2 | 13 | |||||||||
ERBB2 | Wildtype (n = 121) | 42 | 79 | 1.00(0.72–1.00) | 0.65(0.56–0.74) | 0.69(0.60–0.76) | 0.88(0.80–0.96) | 88 | 33 | 0.92(0.62–0.99) | 0.73(0.64–0.80) | 0.75(0.66–0.82) | 0.87(0.78–0.95) |
Mutation (n = 13) | 0 | 13 | 1 | 12 | |||||||||
TP53 | Wildtype (n = 74) | 49 | 25 | 0.80(0.67–0.89) | 0.66(0.54–0.77) | 0.72(0.64- 0.80) | 0.78(0.71–0.86) | 58 | 16 | 0.82(0.69–0.90) | 0.78(0.67–0.87) | 0.80(0.72–0.87) | 0.84(0.78–0.91) |
Mutation (n = 60) | 12 | 48 | 11 | 49 |
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Zhang, T.; Xu, Z.; Liu, G.; Jiang, B.; de Bock, G.H.; Groen, H.J.M.; Vliegenthart, R.; Xie, X. Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics. Cancers 2021, 13, 1814. https://doi.org/10.3390/cancers13081814
Zhang T, Xu Z, Liu G, Jiang B, de Bock GH, Groen HJM, Vliegenthart R, Xie X. Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics. Cancers. 2021; 13(8):1814. https://doi.org/10.3390/cancers13081814
Chicago/Turabian StyleZhang, Tiening, Zhihan Xu, Guixue Liu, Beibei Jiang, Geertruida H. de Bock, Harry J. M. Groen, Rozemarijn Vliegenthart, and Xueqian Xie. 2021. "Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics" Cancers 13, no. 8: 1814. https://doi.org/10.3390/cancers13081814