## 1 Introduction  
Lung cancer frequently manifests in the form of a malignant tumor with very high morbidity and mortality worldwide [1]. In 2015, the World Health Organization integrated multidisciplinary research on lung adenocarcinoma, classifying it into four subtypes based on its different pathologies: atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). With advancements in imaging technology, and the widespread application of computed tomography image (CTI) for lung cancer scan, the detection rate of early-stage lung adenocarcinoma, manifested as ground glass nodules (GGNs), has increased significantly. GGNs are closely related to lung cancer, especially lung adenocarcinoma [2]. During the pathological progress of lung adenocarcinoma from pre-invasive to invasive lesions, GGNs can be observed in the CTI, but lack specificity [3]. The growth of GGN follows a regular pattern, from benign lesions (e.g., AAH) to malignant lesions (e.g., IA) [4]. Most GGN lesions are benign, but about 30% are malignant including AIS, MIA, and IA [5]. However, GGN is likely to be related to other lung diseases such as viral pneumonia [6], the coronavirus disease 2019 (COVID-19) [7], etc., where COVID-19 has become a global pandemic. To date, how to distinguish their differences remains a key issue. In the past, traditional computer-aided diagnosis methods utilize various feature extraction protocols to quantify the appearance of nodules on diagnostic CTIs, and machine learning algorithms such as fuzzy clustering [8], threshold segmentation [9], support vector machines [10], etc., have been employed to classify GGNs. Although these works have achieved impressive performance, extracting appropriate nodule features is very time-consuming, unapparent, and unclear. In recent decades, three classes of remarkable progresses on GGN have been made as follows. (1) Rapid development in digital imaging and artificial intelligence technologies has led to the field of radiomics, a new technique first proposed by [PERSON] et al. in 2012 for the noninvasive diagnosis of tumors [11]. Radiomics is recognized as an effective quantitative tool for characterizing the phenotypes of lung lesion [12]. It has achieved remarkable results in oncology assessments and diagnosis as well as in post-treatment prognosis [13]. In early pulmonary nodules diagnosis, for example, studies have demonstrated that radiomics performs well when classifying benign or malignant pulmonary nodules, histopathologic lung cancer phenotypes, and invasiveness in lung adenocarcinoma lesions based on quantitative CTIs [14,15]. (2) Deep learning (DL) methods have been demonstrated to greatly reduce the difficulty of feature extraction in CTIs [16]. Unlike the radiomics model, the DL-based model can extract deep imaging features by using an end-to-end deep convolutional neural network [17]. [PERSON] et al. [18] showed that DL combined with the radiomics features could conveniently and automatically obtain the best performance in predicting the invasiveness of lung adenocarcinoma manifesting as GGNs. Moreover, a cascade architecture with both segmentation and classification networks was built. It could perform better and was more stable than the multi-task learning model appearing as GGNs. [PERSON] et al. [19] proposed an automatic GGN invasiveness classification algorithm for the adenocarcinoma. Experiments showed that the algorithm outperformed the traditional machine learning method. (3) AI techniques have attracted significant attention in the fight against COVID-19. One crucial application to use CTIs is to segment the COVID-19 infections, which can aid doctors in the treatment. A novel evolvable adversarial framework [20] has been developed for COVID-19 infection segmentation that incorporated the gradient penalty into the network, penalizing the discriminator's gradient norm input.