## 1 Introduction  
Cancer remains a leading cause of death worldwide with nearly 10 million deaths in 2020 and 19.3 million new cases [1,2]. Of the types of cancers, lung cancer is consistently the deadliest, with nearly 20% of these deaths (1.9 million) in 2020 attributable to it alone [1,2]. Identifying cancer early is paramount. When cancers are detected early, the rate of survival increases dramatically. However, studies have found that 50% of cancers detected are already at an advanced stage [3]. Although the prevention of cancer has long been a common strategy for mitigation, strategies have found that it can lead to over-diagnosis and many cancers still lack any preventative strategies at all [4]. This has made the study of cancer diagnosis and treatment of strong interest. Despite many advances in clinical medicine and research the past few decades, the diagnosis of cancer has remained a difficult problem [5]. Current cancer diagnostic strategies often rely heavily on imaging. Techniques and screening through methodologies such as positron emission tomography (PET), computed tomography (CT), Magnetic Resonance Imaging (MRI), and molecular diagnostic techniques have strongly contributed to improved rates of early detection and staging [6]. Lung cancer is typically staged into the tumor, node, and metastasis (TNM-8) staging system through CT scans or a combination of PET/CT to stratify the risk and severity of the tumor [7]. As there are currently no validated biomarkers for lung cancer for the prediction of which patients need treatments such as radiotherapies, there has been growing interest in radiomics, which looks to find quantifiable imaging features that could lead to a greater stratification of the risk and severity of lung cancer tumor [8]. The use of deep learning in combination with traditional radiomics analysis has also grown in popularity due to its potential to diagnose and further stratify risk of cancers such as lung cancer [9,10]. The rise of genetic testing and precision medicine has brought with it great potential to unlock more methods of cancer diagnosis and treatment. After the first human genome was sequenced in 2005, various studies have attempted to identify the specific genomic markers of cancer and, especially, lung cancer [11, 12, 13, 14, 15, 16]. Radiogenomics, or the combination of identifying these genetic factors on traditional imaging techniques, has become a popular potential new methodology and target of cancer research. Radiogenomics offers a noninvasive, faster, genetic screening panel for cancer and attempts to identify these predictive genetic biomarkers from cancer imaging [17, 18, 19]. The radiogenomics workflow typically consists of the collection of a large group of imaging features such as contrast, intensity, heterozygosity, dynamic features, or software-identified markers from a medical image [20]. Next, these features need to be filtered to a manageable set for potential analysis through deep learning or traditional statistics. Pilot studies have evaluated the use of clustering for the radiogenomic analysis of lung cancer using groups of 100-500 patients to attempt to group these features and simplify the total number of model inputs or to separate the outcome variable to simplify analysis [21, 22, 23]. Current approaches often are demonstrated and validated through simple statistic tests such as \(t\)-tests with corresponding \(p\)-values to analyze input variables selected by models and the outcomes [24]. This deep learning based framework has so far demonstrated success for predicting treatment outcomes for cancers such as lung cancer [25, 26]. But this methodology is not without limitations.