# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 1 Introduction
[PERSON] et al., trying to address such problems, compared many feature selection techniques and predictive models to improve the radiomics-based prognosis of patients with non-small cell lung cancer (NSCLC) [26]. With respect to the problem of feature redundancy, their analysis showed that Random Forests (RF) were the optimal predictive model, whereas Principal Component Analysis (PCA) was the optimal feature selection method. The Synthetic Minority Over-sampling (SMOTE) technique was employed to mitigate the problem class unbalancing, significantly increasing the predictive accuracy. In this work, we designed and implemented a quantitative approach based on a radiomics pipeline to classify lung nodules between adenocarcinoma (LUAD) cases and other histological classes from unenhanced CT images. The overall radiomics pipeline consists of the following stages, as reported in Figure 1. First, we collected CT images of patients with lung cancer in a cohort from the _Azienda Ospedaliero--Universitaria \"Policlinico Riumiti\"_ in Foggia, Italy. Images were manually segmented by expert radiologists to identify and extract the Regions of Interest (ROIs) to process. Radiomic features were then extracted from the ROIs. To do this, we used PyRadiomics, an open-source python package for the extraction of radiomics features from medical images [27], which also has the advantage of increasing reproducibility among different studies, thanks to the adoption of the Imaging Biomarker Standardization Initiative (IBSI) [13], to define and compute the features. Statistical analyses for selecting features based on their discriminative power were accomplished, and predictive models were set up on the extracted feature data to make the decision on the histological subtype. Finally, external data were considered to validate the predictive model. Concerning the task of lung cancer phenotyping, several works could be found in the literature. For example, [PERSON] et al. studied which quantitative features coming from contrast-enhanced CT scans would be more helpful in performing associations with histopathological data, such as the histological subtype classification [28]. However, contrast-enhanced imaging analyses include, albeit minimal, risk factors also related to the injection of the contrast medium. To overcome this, in this work, we analyse radiomic features on unenhanced CT scans for making decisions. [PERSON] et al. also included unenhanced CT scans in their work [29]. To demonstrate the validity of their approach, the authors performed a ten-fold cross-validation. However, validating the models on external and independent datasets is crucial in radiomics studies [30]. Instead, a similar work, which validated predictive models considering external data, was the one by [PERSON] et al. [31]. The authors, in fact, developed a Naive Bayes classifier and validated it with external datasets. However, in their work, [PERSON] et al. considered images acquired in the Netherlands only. In our work, instead, external validation is performed using image data obtained in different countries. Figure 1: Radiomics workflow. The complete workflow includes the segmentation of the lung region, filtering to enhance the image, extraction of radiomics features, classification models and statistical analysis. To do this, we included in our analysis data from two cohorts employed in the work by [PERSON] et al., who made it publicly available in terms of clinical data, radiomics features and genetic characteristics [32]. [PERSON] et al. discovered a relationship between imaging features, immune response, survival and inflammation. They found that imaging features have predictive value for specific pathways; also, they concluded that a combination of clinical information, radiomics and genetic biomarkers improve the prognostic predictive performance, showing the complementary value of these data [32]. In this work, we considered only the radiomics features.