Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer

Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.


Materials and Methods
Participants. We retrospectively reviewed data for a total of 146 lung cancer patients with BMs who underwent gadolinium-enhanced brain MRI at Gangnam Severance Hospital between June 2012 and July 2018. We excluded 85 patients for the following reasons: (1) previous neurosurgery or brain radiation therapy (n = 21), (2) presence of other malignant disease (n = 11), (3) poor image quality (n = 7), (4) absence of EGFR mutation status (n = 20), and (5) no measurable BM (n = 26). We regarded a BM as measurable when its diameter was greater than 3 mm, as it is difficult to differentiate BMs with a diameter of less than 3 mm from adjacent vessels. A total of 61 patients with 210 measurable BMs remained after exclusion. The institutional review board of Gangnam Severance Hospital approved this retrospective study and waived any requirement for informed consent because of its retrospective nature. All data were fully anonymized, and all experiments were carried out in accordance with approved guidelines.
Pathology and EGFR mutation analysis. All patients had histopathological diagnoses of lung cancer by bronchoscopic, percutaneous needle-guided, or surgical biopsies. Genomic DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tissues using the DNeasy Isolation Kit (Qiagen, Valencia, CA, USA). We used the PNA Clamp TM EGFR Mutation Detection Kit (PANAGENE, Daejeon, Korea) for detection of EGFR mutations by real-time PCR 42 .
Image processing and extraction of radiomics features. T1-enhanced images were processed with the following steps: preprocessing, feature extraction, feature selection, and classification. For preprocessing, nonuniformity was corrected using the N3 bias correction algorithm, re-orientation was applied for further analysis using FMRIB Software Library (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki), and cropped images including tumor volume were generated by a neuroradiologist (S.J.A) (Fig. 1). All imaging data were normalized to zero-mean and unit-variance to reduce bias. Radiomics features were extracted using MATLAB R2014b (MathWorks), in accordance with previous studies 18 . The 1209 resultant radiomics features comprised three feature groups: six first-order, 25 second-order, and 1178 higher-order features. First-order features were based on intensity profile histograms (e.g., for mean, variance, skewness, kurtosis, energy, and entropy, Supplemental Table 1). Second-order features were based on texture analysis consisting of 25 features 25,43,44 (Supplemental Table 2). For higher-order features, 38 feature maps were created using the root filter set filter bank (Supplemental Table 3) 45,46 . Six first-order and 25 second order features were also generated for each feature map (1178 features).
Feature selection and classification methods. A ten-fold validation method was applied to the data set (training set = 189, test set = 21). Feature selection was performed with a training set. A two-sample t-test of positive and negative classes was used for each feature to select the most discriminative features, to prevent overfitting, and to reduce feature space dimensions. Seven different feature selection algorithms were used for further feature selection: permutation random forest 34 , 0-norm minimization 35 , infinite feature selection 36 , a feature selection via concave minimization 37 , minimum redundancy maximum relevance 38 , relief 39 , and Laplacian 40 .
Classification was performed with four different powerful algorithms to improve diagnostic performance for prediction of EGFR mutation: RF, support vector machine (SVM), adaptive boosting trees, and LASSO-regularized logistic regression [47][48][49][50] . These methods were chosen largely based on their common uses in previous studies and readily available implementation. Models were reestablished with features that were identified in the training set and then applied to the test set. Diagnostic performance was calculated using area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity. A subgroup analysis was performed depending on the size of the metastases (small vs. large). The diameter of small BMs was defined as less than 10 mm (n = 137) and that of large BMs was more than 10 mm (n = 73). For small BMs, ten-fold cross-validation was also used. However, for large BMs, the "leave one out method" was used to maintain a sufficiently large training dataset 51 .

Statistical analysis.
To evaluate a statistical significance of the classification performances, the permutation test was performed with a similar framework performed in previous studies 52,53 . We randomly permuted the group labels 500 times. In each permutation, the 10-fold cross-validation process was performed based on the permutated samples to calculate the AUCs. We defined p-value as follows; P -value = (1 + number of time achieving higher AUCs than true lables) / 501(the number of all tests including the original one) A threshold level of 0.05 was established for significance.

Results
Patient characteristics. Patient characteristics are summarized in Table 1  www.nature.com/scientificreports www.nature.com/scientificreports/ and EGFR mutation, respectively, p = 0.31). The total number of large BMs was 73 (41 for EGFR wild type and 32 for EGFR mutation). The mean diameters of measurable BMs were 19.6 ± 6.4 and 22.2 ± 10.5 mm (EGFR wild type vs. EGFR mutation, respectively, p = 0.24).
Diagnostic performance. Using radiomic features, individual combinations of the seven selection features and four classification methods showed different EGFR diagnostic performances (AUC) for lung cancer BM (Fig. 2). The random forest classification using random forest selection demonstrated the highest AUC (86.81, p < 0.01). The sensitivity, specificity, and accuracy of this method were 84. 41 Table 1. Lung cancer patient with brain metastases. brain metastases (BM); epidermal growth factor receptor (EGFR). www.nature.com/scientificreports www.nature.com/scientificreports/ respectively. AdaBoost with mRMR and RF with RF also had good diagnostic performances (AUC: 87.37 and 87.12, respectively). However, LASSO-LR using RF selection exhibited relatively poor diagnostic performance (AUC: 64.16, Table 3).  Table 2. Diagnostic performance of contrast-enhanced T1-weighted image radiomic-based prediction of EGFR mutation status in lung cancer brain metastases cases. Epidermal growth factor receptor (EGFR); area under the curve (AUC); random forest (RF); support vector machine (SVM).   www.nature.com/scientificreports www.nature.com/scientificreports/ For large BMs, SVM classification with RF selection demonstrated the highest AUC of 78.22 (Fig. 3b). The sensitivity, specificity, and accuracy of this method were 62.96, 93.47, and 82.19, respectively. AdaBoost with Relief and RF with Laplacian had similar diagnostic performances (AUC: 76.48 and 76.04, respectively). However, LASSO-LR with L0 demonstrated relatively poor diagnostic performance (AUC: 57.85, Table 3).

Discussion
Tumor radiomics utilizes advanced computational methods to convert medical tumor images into a large number of quantitative features 54 . In the present study, we used seven feature selection methods and four classification methods to extract 1209 features from contrast-enhanced T1 images of 210 BMs. We analyzed the potential value of these features for predicting EGFR mutation status in primary lung cancer cases. We found that radiomics could be used to predict EGFR mutation status with high diagnostic validity. However, LASSO-LR demonstrated relatively poor diagnostic performance, compared with the other classification algorithms tested. Furthermore, diagnosing EGFR mutation status in large BMs (diameter > 10 mm) was not as effective as that in small BMs.
EGFR is a transmembrane protein with cytoplasmic kinase activity that transduces important growth factor signaling from the extracellular milieu into the cell 11 . Patients with lung cancer and BMs harboring EGFR mutations exhibit better responses to treatment as well as different clinical features. For example, the number of BM lesions was significantly higher in patients with EGFR-mutated NSCLC than in those with wild-type NSCLC. Moreover, leptomeningeal metastases were more common in patients with EGFR-mutated NSCLC 55 . A recent study proposed an imaging biomarker for the non-invasive determination of EGFR mutation status. Jung et. al reported that the minimum apparent diffusion coefficient (ADC) and normalized ADC ratio of BMs could be independent predictors of EGFR mutation status 17 . However, diffusion weighted images, which are used to calculate ADC variables, are not a routine sequence in BM protocols and parameters may thus vary between institutions. Meanwhile, contrast-enhanced T1 imaging is a common sequence in BM protocols because it is often used to delineate tumor margins and to monitor tumor responses to therapy. The clinical relevance of our results lies in the development of a novel imaging biomarker for BM EGFR mutation status in lung cancer patients. Of particular interest, this biomarker may be extracted from a commonly used sequence.
The high performance of EGFR mutation status prediction by our model can be explained by multiple factors. First, we generated first-, second-, and higher-order features using a root filter set filter bank. Higher-order features have been reported to help with capturing characteristic features: For example, one study found effective segmentation of white matter hyperintensities using a texton filter bank 56 . Furthermore, high-order CT features extracted through LoG and wavelet filters were used successfully to quantify non-small cell lung cancer phenotypes 21 . Second, we used a combination of several feature selection and data mining methods to achieve superior diagnostic performance.
Our results indicate that RF, AdaBoost, and SVM had good diagnostic performance, while LASSO did not. RF and AdaBoost are ensemble learning paradigms, which make predictions based on a number of different decision trees. However, their methodologies differ slightly. RF trains on multiple random subsets of features in a parallel way to arrive at a final conclusion 34 . Meanwhile, AdaBoost is trained on a number of decision trees sequentially, and each decision tree learns from mistakes made by the previous tree 57 . Generally, prediction variance decreases when the number of trees in the ensemble method increases. These models are insensitive to overfitting, which might explain their good performance 58 . SVM classifies by finding the hyperplane 59 . The hyperplane is calculated from the nearest training samples, called support vectors (SVs) and is optimized by maximizing the margin between the positive and negative SVs. As predicting EGFR status is a two-class problem (wild type or mutant), SV may be best suited for the purposes of the present study. LASSO is a variable selection algorithm used in regression models 50 . It adds a penalty equal to the absolute value of the magnitude coefficients. LASSO is a linear method and is preferred when true decision boundaries are linear. Thus, it appeared to struggle with handling nonlinear relationships in the data here. Given that LASSO had relatively poor performance in the present study, the relationship between the radiomics of contrast-enhanced T1WI of BMs and EGFR status is likely non-linear.
We identified RF as the most powerful selection tool of those tested here, regardless of classification method. RF selected related features based on importance scores, which are derived from how pure each feature is through numerous yes-or-no questions 34 . This process involves numerous decision trees, each of which is built via the random extraction of multiple features. Not every tree sees all of the features, guaranteeing that trees are de-correlated and therefore less prone to overfitting, a potential strength over other selection methods.
The performance of our model for large BMs was not as good as that for small BMs, which may be explained by several reasons. First, larger BMs tend to have necrotic centers that may affect machine learning classifications 17,[60][61][62] 63,64 . This issue should be further investigated in future work. Second, large BMs are associated with smaller datasets, potentially resulting in overfitting. However, cross-validation techniques and the random forest method diminishes the likelihood of such overfitting 34,65 .
Accumulating evidence suggests that there are clinico-pathological features that are closely related with EGFR mutations. Mutations have been shown to be associated with Asian ethnicity, adenocarcinoma histology, female sex, and non-smokers 11,66 . On the basis of results from a large study, these clinico-pathologic features of EGFR seem to be consistent in patients with lung cancer BMs 67 . In our results, the EGFR mutation group comprised more females and adenocarcinomas than the EGFR wild-type group, but the differences did not reach statistical significance. Thus, a combined model of clinico-pathologic features and radiomic model may enhance diagnostic performance for predicting EGFR mutation status in lung cancer BMs from larger populations which is expected to be validated in future study.
The present study has limitations that warrant consideration. Genetic testing was performed on lung samples rather than BMs themselves. Recent studies have revealed that EGFR mutation status in metastatic lesions does (2020) 10:8905 | https://doi.org/10.1038/s41598-020-65470-7 www.nature.com/scientificreports www.nature.com/scientificreports/ not always coincide with that at primary sites 55,68 . Indeed, discordant rates of EGFR mutation status between primary lung cancer and BM in previous studies range from 0 to 66.7% [69][70][71][72][73][74][75] . According to meta-analysis, the EGFR discordance rate between primary tumor and central nervous system is 17.26% (95% CI = 7.64 to 29.74) 76 . There are several models that might explain the discordance of EGFR mutation between primary lung cancer and BM. Cancer cells with highly diverse genetic profiles might be disseminated to distant organs at an early stage, or EGFG mutation status might change though multistep metastatic progression, potentially due to influences from the microenvironment and treatment effects. Thus, further study of tissues obtained directly from brain lesions or animal model with EGFR mutation is necessary to reveal the molecular and biologic characteristics of BMs more precisely. However, we believe our result has a clinical impact because it may aid in clinical decision for first-line treatment of lung cancer. The incidence of BMs in the patients with NSCLC at initial diagnosis is approximately 10% 4 . On the basis of this report, routine brain MRI screening scan is performed in many institution. Majority of BMs in our cohorts were also diagnosed at initial screening scan (48/61, 79%). In this perspective, our result may provide an alternative method to non-invasively assess EGFR information of primary lung cancer and offers a great supplement to biopsy, thereby making a proper first-line treatment of lung cancer. Also, our result is novel as it provides a different approach with previous other efforts using chest CT scan 77,78 .
In conclusion, we demonstrated here that T1-enhanced radiomics using RF classification may predict EGFR mutation status in lung cancer BMs with a high degree of accuracy. However, further study is necessary to apply T1-enhanced radiomics to large BMs.