Abstract
Objectives
The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models.
Methods
Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.
Results
The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point.
Conclusion
Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool.
Key Points
• Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status.
• In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions.
• The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.
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Abbreviations
- 19 del:
-
Exon 19 deletions
- AI:
-
Artificial intelligence
- ALK:
-
Anaplastic lymphoma kinase
- AUC:
-
Area under the receiver operating characteristic curve
- CT:
-
Computed tomography
- CTC:
-
Circulating tumor cells
- ctDNA:
-
Circulating tumor DNA
- EGFR:
-
Epidermal growth factor receptor
- exon 21 L858R:
-
Exon 21 amino acid substitution at position 858 in EGFR, from a leucine (L) to an arginine (R)
- GGO:
-
Ground glass components
- IASLC:
-
The International Association for the Study of Lung Cancer
- NSCLC:
-
Non-small cell lung cancer
- PCR:
-
Polymerase chain reaction
- RF:
-
Random forest
- ROC:
-
Receiver operating characteristic
- ROI:
-
Region of interest
- TKI:
-
Tyrosine kinase inhibitors
- TPS:
-
Treatment planning system
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Acknowledgements
The abstract of this manuscript was published in the meeting of IASLC 18th World Conference on Lung Cancer with a smaller patient set.
See:
Tian-Ying Jia, et al, Detecting Epidermal Growth factor receptor mutation status in patients with lung adenocarcinoma using radiomics and random forest. IASLC 18th World Conference on Lung Cancer, MA 14.12.
Funding
This study has received funding by National Key Research and Development Program of China (2016YFC0905502 and 2016YFC0104608), National Natural Science Foundation of China (No. 81371634), Shanghai Jiao Tong University Medical Engineering Cross Research Funds (YG2017ZD10, YG2013MS30, and YG2014ZD05), and the project of multi-center clinical research, Shanghai Jiao Tong University School of Medicine (DLY201619).
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The scientific guarantor of this publication is Xiao-Long Fu.
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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
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No complex statistical methods were necessary for this paper.
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Written informed consent was not required for this study because this study used CT images and clinical information and did not involve treatment decisions.
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Institutional Review Board approval was not required because this is a retrospective study and did not influence the treatment of the patients.
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• retrospective
• diagnostic study
• performed at one institution
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Jia, TY., Xiong, JF., Li, XY. et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. Eur Radiol 29, 4742–4750 (2019). https://doi.org/10.1007/s00330-019-06024-y
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DOI: https://doi.org/10.1007/s00330-019-06024-y