## 2 Materials and Methods  
### Binary Classification for Thoracic Cancers  
For the prediction of thoracic cancers, we used a dataset collected by the Thoracic Surgery Clinic of the Municipal University Hospital in Timisoara. It is composed of real data collected from 100 patients, including 55 patients that have no cancer and 45 patients who suffer from cancer. Among the ones with cancer, the aim is to discriminate against the ones with surgical recommendations, as in the case of non-small cell lung cancer. In this case, our ground truth was the histopathologic result. The clinical features that were present in the dataset were 19 in total and can be seen in Figure 1, from which the links between each two entries can be analyzed (see Figure 1). The value of a correlation coefficient ranges between \(-1\) and \(+1\), with \(-1\) indicating a perfect negative correlation, \(+1\) indicating a perfect positive correlation, and 0 signifying no correlation at all. We computed the [PERSON]'s correlation coefficient based on the actual clinical values of the features. To train our neural network, we considered 16 clinical (a-f) and imaging (f-p) parameters that are associated with a higher cancer probability, as follows:  
1. Age: It is known that lung cancer has a greater incidence after the age of 45 years;
2. Gender (sex): So far, there is a higher incidence of lung cancer observed in men compared to women in all countries, regardless of the country's development status;  
Figure 1: Correlation matrix for the thoracic dataset. * Smoking status: It is well proven in the literature that smokers have a very high risk of lung cancer compared to non-smokers;
* Exposure: The scientific literature has demonstrated the role of passive smokers. Furthermore, exposure to microparticles and pollutants has a direct role in lung cancer's etiology;
* Medical history (historic): refers to prior chronic lung illness that could be related to lung cancer, such as tuberculosis or prior chemotherapy that could be the main cause of malignant transformation of the lung cells;
* Number of nodules (NoNoduls): A CT scan may show a single solitary lung nodule, but in many patients, the nodule can be associated with one or more extra nodules in the same lobe or lung, suggesting malignancy or metastatic disease. The imaging characteristics of the nodules are very important and will be discussed below;
* Localization (PeHi): Central localization is associated with a higher prevalence of cancer as opposed to peripheral nodules;
* Size: The relationship between the size of the solitary pulmonary nodule (SPN) and malignancy rates are as follows: 1% for SPNs less than 5 mm, 6-28% for those between 5 to 10 mm, and 64-82% for those larger than 2 cm;
* Calcification: This is usually a sign of benign disease. There are 6 types of calcifications associated with benign disease: central dense nidus, diffuse solid, and laminated, which are usually encountered in granulomatous lung disease (sarcoidosis or TB); laminated and popcorn, which are associated with lung hamartomas; and punctuate and dendriform, which are not always associated with benign disease and have been described in carcinoid tumors, metastasis, and primary bronchogenic carcinoma;
* Fat within the nodule: CT SPNs that have fatty HU density are usually associated with pulmonary hamartoma, lipoid pneumonia, or lipoma and are considered signs of benignity. * Margins (borders): The margins of the lung nodules are classified as follows: (1) sharp and smooth; (2) moderately smooth; (3) undulated borders or minimal spiculation; (4) gross marginal spiculation. However, although the margins and spiculations of SPN are associated with malignancy, they are not specific and must correlate with other factors;
* Cavitation (cavity): If present, the cavitation margin's thickness is suggestive of malignancy: (1) a wall size under 1 mm is not associated with cancer; (2) 5-15 mm is malignant in 49% of cases, and (3) over 15 mm is almost always malignant.