# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 1 Introduction  
Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide [1]. According to the WHO, stroke is the second leading cause of disability and death worldwide after ischemic heart disease, accounting for 11% of all deaths. With improvements in the quality of life and longevity of patients, the incidence of stroke is expected to continue to rise in the coming years [2]. More than 80% of strokes are ischemic in origin [3]. Recent advances in the management of these patients have focused more on their treatment, where endovascular therapies have radically changed the prognosis of these patients [4; 5; 6]. In recent years, numerous studies have explored the many diagnostic aspects of ischemic stroke. However, since the introduction of cerebral perfusion and diffusion imaging more than a decade ago, technological advances with clinical applicability have been somewhat stagnant [7].  
Today, medicine is immersed in a digital world. Specialties such as radiology are growing in demand. In recent years, with the advent of artificial intelligence, applications such as radiomics have emerged to harness this technological revolution and improve diagnostic techniques in the search for personalized clinical management of patients. Since the coining of the term radiomics in 2012 [8], articles on this topic have become increasingly common.  
Radiomics is a radiological discipline based on the idea that images are more than just pictures and that there is a lot of data that is invisible to the human eye, but which we can quantify and analyze by looking for data patterns associated with certain pathologies. Initially, radiomics was mainly focused on the field of oncology [9]. All cancer patients have at least one staging CT scan, so the most commonly used imaging technique for feature extraction in these studies was the CT [10]. The aims of these studies have been diverse, ranging from identifying prognostic factors or estimating overall survival to predicting response to treatment. The use of radiomics for gene determination (also called radiogenomics) has also been developed in pathologies such as brain tumours [11], lung cancer [12] and abdominal tumours [13]. In recent years, the use of radiomics has even been extended to other imaging modalities such as MRI, positron emission tomography (PET) based on both CT and MRI, and ultrasound.  
In the field of neuroradiiology, radiomics features obtained from magnetic resonance imaging (MRI) were used most frequently in the articles. In these articles, radiomics also focused mainly on oncological patients. The objectives ranged from differentiation of high-grade gliomas from brain metastases, prediction of EGFR mutations, or prognostic prediction, to determination of overall survival. In recent years, the use of radiomics has extended to brain pathologies other than tumours. There are articles on the use of radiomic features for prognostic prediction in acute ischemic stroke (AIS) [14], for predicting degenerative diseases [15], or even for predicting aneurysm rupture [16].  
The aim of this review is to summarize published articles on the performance of radiomics in AIS patients. In addition, we aim to determine whether radiomics can provide valuable information for the clinical management of AIS patients. We are at a key moment in the evolution of radiology, as we have seen with other technological advances such as diffusion MRI, helical CT or, more recently, dual-energy CT. We need to be at the forefront of this technological evolution so that our patients can benefit.