## 1 Introduction  
According to the World Health Organization (WHO), lung cancer is both the most common and the most lethal form of cancer globally. In 2022, worldwide, 2.5 million new cases were registered and 1.80 million deaths were attributed to lung cancer [1]. Population growth and ageing lead to the rapid growth of diagnosed cancers and cancer-caused mortality [2]. The most common risk factors, namely air pollution, genetic predispositions, history of respiratory diseases and smoking, also contribute to this growth [3]. These several factors can interact, resulting in a pathological ecosystem with a complex causal process that affects prevention and patient therapeutic, in similarity with other types of cancer [4]. Lung cancer tumours can be divided into two histological groups: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). NSCLC is the most prevalent type of lung cancer, with an occurrence of 80% to 85% [3]. This type of cancer is divided into three different subtypes: adenocarcinoma (ADC), with a prevalence of 40%; squamous cell carcinoma (SCC), with a prevalence of 25%; and large cell carcinoma (LCC), responsible for 15% of the cases [3]. The less common subtypes, which have different characteristics from the aforementioned subtypes, can be aggregated in the \"not otherwise specified\" (NOS) subtype which accounts for 20% of the cases [5]. The prognosis and treatment of a malignant nodule can be impacted by the age and general health of the patient, cancer stage, tumour genetics, size, and subtype [5,6]. The histological subtype directly conditions the patient management, treatment, and survival [6; 7; 8]. For this reason, determining the tumour subtype can be of crucial importance and is performed by biopsies or by the pathological analysis of tissue recovered during resection surgery. This is an invasive procedure for the patient and a costly time-consuming process for the pathologist and sometimes requires the processing of multiple samples due to tumour heterogeneity [5,6]. The importance of the subtype identification process for patient treatment and prognosis demands a non-invasive method capable of being an alternative to a tissue biopsy. The use of CT scans does not impact the clinical workflow since these types of scans must be performed before the biopsies for localizing the lung nodules. Currently, in clinical routine practice, the non-invasive imaging method used for the diagnosis and location of tumours, even small ones, is computed tomography (CT). Different tumour subtypes can be found in the CT image with different phenotype characteristics, such as size, morphology, margin irregularity or tumour opacity, which can be used to classify the tumour subtype and malignancy [5,9; 10]. This classification task is, however, very reader-dependent and influenced by the knowledge, experience, and analytical skills of the radiologists [5,9]. This reveals the need for an automatic process capable of supporting radiologists to perform tumour classification with high accuracy and through a non-invasive procedure. The use of lung CT images allows the construction of predictive models capable of classifying the tumour subtype, which can be incorporated into the clinical workflow to support the decisions of radiologists [11]. This work aims to study how lung CT images can be used to construct a machine learning (ML) model capable of predicting lung tumour histological subtypes. The high predominance of the adenocarcinoma subtype resulted in the development of models to classify lung tumours as adenocarcinoma or non-adenocarcinoma. These models can be applied to lung CT images already segmented with the identification of the tumour localization.