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- Title
Computed Tomography‐Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.
- Authors
Yang, Xinguan; Dong, Xiao; Wang, Jiao; Li, Weiwei; Gu, Zhuoran; Gao, Dashan; Zhong, Nanshan; Guan, Yubao
- Abstract
Background: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR‐targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)‐based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule. Materials and Methods: A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT‐based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve. Results: In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT‐based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p >.05). Conclusion: As a noninvasive method, the CT‐based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule. Implications for Practice: Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR‐targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography‐based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible. Persistent subsolid nodules are known to represent early‐stage lung adenocarcinoma, and epidermal growth factor receptor (EGFR) gene mutation is commonly involved in the occurrence, development, and metastasis of tumors. The aim of this study was to develop and validate predictive models to differentiate EGFR mutations for subsolid lung adenocarcinoma.
- Subjects
ADENOCARCINOMA; BIOMARKERS; COMPUTED tomography; DIAGNOSTIC imaging; EPIDERMAL growth factor; DIGITAL image processing; LUNG tumors; COMPUTERS in medicine; GENETIC mutation; ONCOGENES; PROTEIN-tyrosine kinase inhibitors; RECEIVER operating characteristic curves; SOLITARY pulmonary nodule
- Publication
Oncologist, 2019, Vol 24, Issue 11, pe1156
- ISSN
1083-7159
- Publication type
Article
- DOI
10.1634/theoncologist.2018-0706