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

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.

Figure 1

The participant recruitment process MRI, magnetic resonance imaging; EGFR, epidermal growth factor receptor.

Figure 2

The radiomics analysis workflow Multiple-sequence MR images were selected and manually contoured. The radiomic features were extracted and selected from processed images to build models to predict the EGFR status of brain metastases. The performance of the models was evaluated using an independent test set. T1CE, contrast-enhanced T1-weighted imaging; T2WI, T2-weighted imaging; T2 FLAIR, T2 fluid-attenuated inversion recovery; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic curve.

Figure 3

The EGFR mutation status distributions of primary lung cancers and paired metastases Overall, the EGFR status showed a discordance rate of 15.4% between the primary cancer and the matched brain metastases. The number of patients is provided in parentheses. EGFR, epidermal growth factor receptor.

Figure 4

Confusion matrix (A) and ROCs (B) for the classification of EGFR mutation status in the test set The confusion matrix was generated using a combined model. The combined model appeared to achieve a higher AUC than any individual sequence, but the differences were not statistically significant. EGFR, epidermal growth factor receptor; ROC, receiver operating characteristics curve; AUC, area under the curve; T1CE, contrast-enhanced T1-weighted imaging; T2-FLAIR, T2 fluid-attenuated inversion recovery; T2WI, T2-weighted imaging; combination, combined model extracting features from the three sequences.

Figure 5

The decision curve analyses of various models The best decision benefit was observed with the combined model. T1CE, contrast-enhanced T1-weighted imaging; T2-FLAIR, T2 fluid-attenuated inversion recovery; T2WI, T2-weighted imaging; combination, combined model extracting features from three sequences.