## 1 Introduction  
Lung cancer is the foremost determinant cancer death amongst men and women, killing a vaster number of people than colon, breast, and prostate cancers combined [1]. This is linked to the fact that it is often diagnosed in an advanced stage, with 5% or less chance of a 5-year survival [2]. Non-Small-Cell Lung Carcinoma (NSCLC) is the most prevalent histological type of lung cancer, covering about 85% of all lung cancer cases [3], and Epidermal Growth Factor Receptor (_EGFR_) is the most frequently mutated gene that springs lung cancer of type Adenocarcinoma [4]. The cell surface receptor _EGFR_ is responsible for cell growth and survival and its mutations promote _EGFR_ permanent activation, which contributes to uncontrolled cell division [5; 6]. This genomic biomarker with clinically approved therapies is now considered a strong prognostic indicator in lung cancer, rising opportunities to explore treatment strategies that rely on the individual's genetic profile [7]. Currently, biopsy is the primary method for characterizing lung cancer and identifying _EGFR_ mutation status, using an extracted tumor tissue sample for molecular analysis. Nevertheless, this invasive procedure can lead to some associated side effects, as it can be painful and risky for the patient. Recently, blood-based screening has been used to detect early lung cancer diagnostic biomarkers [8]. However, despite being less invasive than biopsy, this technique is still bothersome for the patient. Computed tomography (CT) scans provide a reliable lung cancer characterization, offering a faster and less invasive approach compared to traditional tissue biopsy [9]. Thereby, foretelling gene mutation status by CT can help determine the most appropriate treatment for each subject, while decreasing medical complications [10]. Extracting quantitative features from CT images that are then used as inputs within a predictive model that can directly classify the _EGFR_ mutation status for lung cancer patients composes the essence of radiogenomics, a field that correlates the radiomic features (image phenotype) and genetic information (genotype) [11]. There are a few studies that used traditional statistical analysis and machine learning (ML)-based approaches to demonstrate that the _EGFR_ mutation status is correlated with CT scan imaging phenotypes. Regarding the works that employed logistic regression models, [PERSON] et al. [12] obtained an Area Under the Curve (AUC) of 0.73 applying only radiomic features, which increased to 0.79 when clinical data was added. The study by [PERSON] et al. [13] resulted in an AUC of 0.58 and 0.66 implementing only radiomic features and combination of radiomic and clinical features, respectively. Similarly, [PERSON] et al. [14] showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, as the AUC increased from 0.67 to 0.71, and [PERSON] et al. [15] showed that using clinical variables combined with CT features (AUC = 0.78) resulted in higher AUC values, compared to using clinical variables alone (AUC = 0.69). Concerning the ML domain, a decision tree was built to predict the presence of _EGFR_ mutations using a combination of four image features (emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin), resulting in a test set performance of 0.89 AUC [5]. A work based on a deep learning technique with automatic nodule radiomic feature-learning ability achieved an AUC of 0.69 and showed a significant improvement when hand-crafted CT features were combined with clinical characteristics (AUC = 0.81) [10]. [PERSON] et al. [16] found a radiomic signature related to radiographic heterogeneity that successfully discriminated between mutant and wild-type _EGFR_ cases (AUC = 0.69). Combining this signature with a clinical model of _EGFR_ status (AUC = 0.70) significantly improved prediction performance (AUC = 0.75).