# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 1 Introduction  
The Global Cancer Observatory estimated that lung cancer was the leading cause of cancer death in the world's population [1]. Phenotyping lung cancer, or cancer in general, has been demonstrated to be crucial for clinical practice and medical research; in fact, in recent years, a profound effort in designing and employing computational solutions for phenotyping pathologies has been made [2; 3; 4]. Nowadays, such characterisation of pathologies is necessary to move toward the so-called Precision Medicine. It is a modern paradigm for diagnosing or treating cancers, or diseases in general, based on the identification and characterisation of pathology-specificcharacteristics, taking into account the individual variability, i.e., the genetic factors of each subject, their lifestyle and living environment. Made possible also by technical advances in computational sciences, Precision Medicine is definitely revolutionising the healthcare domain. This approach to cancer handling, in fact, aims to precisely target diseases, including lung carcinoma, with a per-subject approach [5]. Medical imaging methodologies, including Computed Tomography (CT), Positron Emission Tomography (PET), or Magnetic Resonance (MR), are crucial for performing clinical tasks related to cancer diagnosis, treatment, or follow up [6; 7; 8]. Medical imaging has already been demonstrated to be essential in the Precision Medicine framework thanks to its capability to improve the knowledge about the clinical phenomenon under consideration (regardless of its nature) [9]. In addition, a considerable amount of recent literature has already shown the capabilities of functional imaging methods to build an in-vivo representation of tumour processes from a biological perspective [10]. This characteristic made imaging methods good candidates for allowing the identification of biosignatures for phenotyping the tumour. Radiomics is an emerging method based on algorithms for data characterization, which allows the extraction of a large number of features from medical images. By exploiting the information carried out by such features, radiomics approaches aim to uncover and quantitatively describe tumoral patterns and characteristics, otherwise not observable through traditional algorithms for image analysis [11; 12; 13; 14; 15]. This new perspective for extracting phenotypic information from imaging, which is already performed for biomedical signals, such as electromyography or electroencephalography [16; 17], may thus provide valuable information for setting up personalised approaches for therapies. Several authors carried out radiomics-based studies in the context of characterising solid cancers [18; 19] or lesions [20] in the lung region. It also allowed the design and implementation of several applications in oncology, such as solid cancer, glioblastoma [21], hepatocellular carcinoma [22] and breast cancer [23] classification, among many others. While the description of images by a large number of features may help in the comprehension of the underlying phenomena, it may also lead to several drawbacks. In fact, due to the high dimensionality of radiomic features, dimensionality reduction and clustering techniques may be needed to improve the classification and generalisation capabilities of automatic systems for supporting decisions [24]. [PERSON] et al. performed analyses to extract clusters of radiomic features and prognostic signatures specific for lung and head and neck (H&N) cancers [25]. In their work, the authors performed consensus clustering before classifying tumour phenotypes, revealing eleven stable clusters of radiomic features for lung tumour classification, also showing associations with clinical parameters. Besides the high dimensionality of data, classification approaches based on radiomics suffer from other data-related problems, including the \"big-p, little-n\" problem, feature redundancy, and class unbalancing [24].