Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases

Lie Zheng; Hui Xie; Xiao Luo; Yadi Yang; Yijun Zhang; Yue Li; Shaohan Yin; Hui Li; Chuanmiao Xie

doi:10.3389/fonc.2022.931812

Radiomic Signatures for Predicting EGFR Mutation Status in Lung Cancer Brain Metastases

Front Oncol. 2022 Jul 14:12:931812. doi: 10.3389/fonc.2022.931812. eCollection 2022.

Authors

Lie Zheng^{1

2}, Hui Xie^{1

2}, Xiao Luo^{1

2}, Yadi Yang^{1

2}, Yijun Zhang^{1

3}, Yue Li^{1

4}, Shaohan Yin^{1

2}, Hui Li^{1

2}, Chuanmiao Xie^{1

2}

Affiliations

¹ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
² Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
³ Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
⁴ Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou, China.

Abstract

Background: Lung cancer is the most common primary tumor metastasizing to the brain. A significant proportion of lung cancer patients show epidermal growth factor receptor (EGFR) mutation status discordance between the primary cancer and the corresponding brain metastases, which can affect prognosis and therapeutic decision-making. However, it is not always feasible to obtain brain metastases samples. The aim of this study was to establish a radiomic model to predict the EGFR mutation status of lung cancer brain metastases.

Methods: Data from 162 patients with resected brain metastases originating from lung cancer (70 with mutant EGFR, 92 with wild-type EGFR) were retrospectively analyzed. Radiomic features were extracted using preoperative brain magnetic resonance (MR) images (contrast-enhanced T1-weighted imaging, T1CE; T2-weighted imaging, T2WI; T2 fluid-attenuated inversion recovery, T2 FLAIR; and combinations of these sequences), to establish machine learning-based models for predicting the EGFR status of excised brain metastases (108 metastases for training and 54 metastases for testing). The least absolute shrinkage selection operator was used to select informative features; radiomics models were built with logistic regression of the training cohort, and model performance was evaluated using an independent test set.

Results: The best-performing model was a combination of 10 features selected from multiple sequences (two from T1CE, five from T2WI, and three from T2 FLAIR) in both the training and test sets, resulting in classification area under the curve, accuracy, sensitivity, and specificity values of 0.85 and 0.81, 77.8% and 75.9%, 83.7% and 73.1%, and 73.8% and 78.6%, respectively.

Conclusions: Radiomic signatures integrating multi-sequence MR images have the potential to noninvasively predict the EGFR mutation status of lung cancer brain metastases.

Keywords: brain neoplasms; epidermal growth factor receptor (EGFR); lung cancer; magnetic resonance imaging; radiomics.