# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics  
### Radiomics Workflow  
The radiomics workflow consists of five consecutive steps (Figure 1):  
* Obtaining images: Radiomics can be applied to all types of medical imaging. There are published articles on the performance of radiomics in ultrasound, Non-Contrast Enhancement-CT (NCECT), Computed Tomography Angiography (CTA), MRI and PET. In this step, there can be a great deal of heterogeneity when using images acquired in different hospitals or even on different machines within the same hospital. Therefore, the acquired images are subjected to a standardization process in which we try to correct this source of heterogeneity [8].
* Pre-processing: The quality of the images can be improved by using the pre-processing tools. In this step, some image filters are used to reduce noise. The aim is to increase the predictive power of the classifiers [17].
* Segmentation: In this step, the region of interest is selected in the radiological image. The segmentation of this region can be performed in three ways: manual, semi-automatic and automatic. Manual segmentation is the gold standard and the most commonly used method in the studies reviewed in this article. The main advantage of this model is the intervention of an expert radiologist in its performance. The main disadvantage of this model is the time required to manually segment the entire region of interest [18]. Automatic segmentation is based on automatic detection of the region of interest without human intervention. Finally, semi-automatic segmentation is performed under the supervision of an expert radiologist who can edit an initial automatic pre-segmentation. The advantage of this method is the speed of the segmentation and the fact that the human component remains [19]. With today's increasingly sophisticated segmentation software, semi-automatic 3D segmentation of an area of interest can be performed quickly and comfortably for the radiologist.
* Feature extraction and classification: There is a wealth of numerical data that can be extracted from medical images, known as radiomic features. There are several classes of radiomic features: Histogram features (grey level mean, maximum, minimum, variance and percentiles), texture features (absolute gradient, grey level co-occurrence matrix--GLCM--, grey level run length matrix--GLRLM--, grey level size zone matrix--GLSZM--, grey level distance zone matrix--GLDZM--, Neighborhood Grey Level Difference Matrix--NGTDM--, and Neighborhood Grey Level Dependence Matrix--NGLDM--), model-based features, transform-based features (Fourier, Garbor and Wavelet) and shape-based features (geometric properties of ROIs) [20]. These numerical data are classified by automatic classifiers. These classifiers are capable of recognizing different groups of patterns depending on the objective we set for them. In this way, the classifiers learn the data patterns with a training cohort. Then, with a test cohort, these classifiers are able to classify the data we provide into the pattern that most closely resembles it. In addition to numerical data, automatic classifiers can be used to input clinical data to search for a combined model.
* Data analysis.