## 1 Introduction  
Lung cancer is the leading cause of mortality by malignant neoplasms worldwide, with an estimated 1.8 million deaths in 2020 [1]. In particular, non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer patients. Computer tomography (CT) is the imaging modality adopted for lung cancer screening (in addition to low-dose CT) and lung cancer diagnosis. Despite the various diagnostic tools and novel treatments, the five-year survival rate for NSCLC is about 22%. Accurate survival prediction following a cancer diagnosis is essential to guide treatment decision making [2]. In this context, machine learning (ML)-based systems could improve patients' outcomes by assisting clinicians with accurate survival analysis, personalized treatment planning, and prognosis [3]. An ML-based diagnostic system traditionally comprises region of interest (ROI) detection or segmentation, feature extraction, and classification [4, 5]. Feature extraction is crucial because the quantitative features extracted from lung images should be relevant and useful for developing applicable classification models. A widely used technique for NSCLC characterization is radiomics, which extracts high-throughput quantitative information from CT lung images [6]. Radionic features comprise morphological, volumetric, histogram-based, and texture parameters that characterize the phenotypic representation of tumors [7]. They also serve as image biomarkers to assess tumor heterogeneity and predict clinical outcomes [8]. Conventional ML radiomic pipelines, composed of feature extraction, reduction, and classification techniques, have been employed in the literature to predict NSCLC survival. The first thorough evaluation of the ML radiomic pipeline was conducted by [PERSON] et al. [9], who investigated fourteen feature selection methods and twelve classification methods to predict NSCLC survival. They found that the Wilcoxon test-based feature selection method and the random forest classifier had the highest prognostic performance, with high stability against data perturbation. In another work, [PERSON] et al. [10] studied the effectiveness of merging five unsupervised feature reduction techniques and eight different classifiers utilizing CT radiomic features from an NSCLC dataset with three clinical outcomes: recurrence, death, and recurrence-free survival. They concluded that factors such as data endpoints, feature selection techniques, and classifiers significantly affect model accuracy, and thus, they should be carefully investigated when performing a radiomic analysis. More recently, [PERSON] et al. [11] tested different ML pipelines for building radiomic models from CT images of NSCLC patients, reporting that the best combination was achieved by the ANOVA feature selection method with a random forest classifier. Additionally, other researchers have delved into diverse ML pipelines and algorithms to enhance prediction models based on CT radiomic features [12; 13; 14]. On the other hand, deep learning (DL) models have achieved remarkable performance in image classification tasks in recent years because of their automatic feature learning capability with different abstraction levels. Therefore, extracting deep features from convolutional neural networks (CNNs) is considered a primary alternative and valuable method to compute image-based parameters that could increase the classification power [15]. They have been used to predict survival in NSCLC patients on CT images [11; 16] and can be combined with the traditional radiomic approach [17]. A disadvantage of radiomic features is the reliance on a human expert to define the set of features, risking missing relevant variables due to ignorance or failure to understand some complex, hidden, or non-intuitive phenomena [18]. This problem has been addressed using DL models; however, a well-known drawback is overfitting, mainly when using small-sized datasets, due to the high number of trainable parameters.