## 3 Radiomics and ML as Powerful Clinical Tools  
Radiomics is a relatively recent area of study, defined by the use of data-characterization algorithms and mathematical tools to extract large amounts of features from manually or automatically segmented volumes of radiographic medical images (magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), CT). These features, mostly inaccessible to the human eye, can be classified into different categories: first-order features describing voxels' intensities or spatial distribution, second-order features comparing relationships between adjacent voxels and third-order features exploring relationships between more than two voxels. Several steps are conducted during the radiomic process, each bearing their own specifications, including image acquisition, data standardization, segmentation, features extraction and qualification, feature selection by stability and filtering, and finally, exploration of association with a selected clinical or paraclinical endpoint (Figure 1). These techniques already display promising results in the field of oncology, for a wide range of diseases [15,16]. Nevertheless, several limitations prevent radiomic toolsto be extensively used in clinical routine, mostly due to the lack of technical standardization and need of validation on independent cohorts. ML is a subset of artificial intelligence in which an algorithm, supervised with labeled data or unsupervised, learns by pattern-recognition and inference from a dataset. ML algorithms are already used in a wide range of applications, and offer significant hopes in the medical field. The goal is to produce a model capable of prediction, prediction endpoints including but not limited to diagnosis, prognosis, treatment decision, evaluation of efficacy and management [17; 18; 19; 20]. As such techniques can simultaneously encompass a large number of variables, they could surpass human decision-making abilities and lead to better patient care as well as to the discovery of hitherto unused parameters [21]. In fact, similar detection rates, or even those that surpass experts, have already been achieved by ML for some applications [22; 23; 24]. Conventional ML techniques include logical regression algorithms, support vector machine (SVM), decisional trees (DT) and Bayesian methods such as naive Bayes classifiers and Bayesian networks. These algorithms can be combined in ensemble learning methods, such as random decision forests (RF), designed to obtain better predictive performance over any of their constituent learning algorithms alone. The development of deep neural networks (DNN), using multiple layers of connected perceptrons, is a more recent step in ML and they are some of the most popular network architectures in use today. While conventional ML techniques require, as input, manually extracted engineered features that quantify predictive information of an image, DNN are able to directly learn such features from the data without specifying them as input. The feature extraction stage thus vanishes, merged with the classification step of the algorithm into a single stage, in which a hierarchical representation is subsequently constructed over the layers of the network. The common limitations for ML techniques lie in the lack of data quality (missing data, duplicated data, labeling errors, etc.) and need of a large quantity of input, in order to better perform and avoid a loss of robustness (especially in the field of radiomics with the large number of features extracted). Each ML method holds respective strengths and weaknesses, taken into account during the selection process for the right approach, depending on the desired outcome. Typically, the development of new ML tools relies on the separation of the original dataset into two independent cohorts: one used by the ML algorithm to learn (training set) and the other used to evaluate the performances on a separate population (testing set).