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Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain
Jiali Xu1, Yuqiong Yang2, Zhizhen Gao3
1Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China; Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China.
Objective:
To explore the feasibility of identifying epidermal growth factor receptor (EGFR) mutation molecular subtypes in primary lesions based on the radiomics features of lung adenocarcinoma brain metastases using magnetic resonance imaging (MRI).
Methods:
We retrospectively analyzed clinical, imaging, and genetic testing data of patients with lung adenocarcinoma with EGFR gene mutations who had brain metastases. Three-dimensional radiomics features were extracted from contrast-enhanced T1-weighted images. The volume of interest was delineated and normalized using Z-score, dimensionality reduction was performed using principal component analysis, feature selection using Relief, and radiomics model construction using adaptive boosting as a classifier. Data were randomly divided into training and testing datasets at an 8:2 ratio. Five-fold cross-validation was conducted in the training set to select the optimal radiomics features and establish a predictive model for distinguishing between exon 19 deletion (19Del) and exon 21 L858R point mutation (21L858R), the two most common EGFR gene mutations. The testing set was used for external validation of the models. Model performance was evaluated using receiver operating characteristic curve and decision curve analyses.
Results:
Overall, 86 patients with 228 brain metastases were included. Patient age was identified as an independent predictor for distinguishing between 19Del and 21L858R. The area under the curve (AUC) values of the radiomics model in the training and testing datasets were 0.895 (95% confidence interval [CI]: 0.850-0.939) and 0.759 (95% CI: 0.0.614-0.903), respectively. The AUC for diagnosis of all cases using a combined model of age and radiomics was 0.888 (95% CI: 0.846-0.930), slightly higher than that of the radiomics model alone (0.866, 95% CI: 0.820-0.913), but without statistical significance (p=0.1626). In the decision curve analysis, both models demonstrated clinical net benefits.
Conclusions:
The radiomics model based on MRI of lung adenocarcinoma brain metastases could distinguish between EGFR 19Del and 21L858R mutations in the primary lesion.
