# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 3 Discussion  
The year 2022 has seen exponential growth in radiomics, which could have been greater or even earlier had it not been for the appearance of COVID-19. Radiomics can be applied to all types of imaging, as we have seen in this review, and it does not require alternative diagnostic procedures to the usual ones for the management of patients with acute ischemic stroke. Of the studies reviewed, most are based on MRI to obtain radiomic features, but we have also seen that CT-based radiomics gives very good results. This, together with the fact that all patients with AIS undergo imaging, means that we have a virtually inexhaustible source of patients, which is likely to increase in the coming years. The application of radiomics to the management of patients with AIS could represent a major diagnostic advance. In the results we have observed in this review, we have seen that radiomic models perform excellently in a wide variety of clinical situations in patients with AIS. For example, radiomics could be able to predict clinical status at 90 days, identify ischemic lesions not yet detectable by the neuroradiologist's eye or by perfusion or diffusion techniques, provide a probability of new AIS, predict the outcome of reperfusion techniques, predict complications of AIS, determine the etiology of AIS, determine the time course of patients with AIS of unknown onset, or differentiate hemorrhagic complications from iodinated contrast extravasation. We find the application of radiomics in predicting the outcome of revascularization, predicting complications after AIS, and predicting etiology of AIS particularly interesting. Radiomics offers a somewhat astonishing range of possibilities, considering the current paradigms of conventional imaging. In particular, the fact that the radiomic analysis of the thrombus may provide information about its etiological origin makes perfect sense, as different clot compositions may imply specific characteristics. Nonetheless, in the author's opinion, the results of other studies should be interpreted with more caution. Currently, the determination of the etiology of stroke is a key point in the management of the AIS patient, as the treatment of such a patient is based on the etiology of the AIS. This lies on several laboratory, clinical and imaging parameters, which frequently imply a several days delay in initiating the specific prevention treatment and sometimes cannot provide a specific diagnosis. Therefore, if radiomics can also detect different data patterns depending on the etiology of the thrombus, we could be looking at a technique that could significantly change the clinical management of the AIS patient. In this respect, it stands to reason that radiomics can offer prognostic information. This could have two possible explanations. On the one hand, if these results were not true, the retrospective design of the studies may have a big influence but can therefore be proven with further prospective studies to rule out any variables that were not analyzed and could act as a bias (such as the different thrombectomy techniques used, the material used, the skill of the neuroradiologist, etc.). This classic drawback of radiomic studies is due to the lack of prospective studies (Krishnan, 2014), which could be explained by the recent emergence of these radiological techniques in medical research. On the other hand, if the results are surprisingly true, this could be derived by the intrinsic characteristics of the thrombus, by its frailty and susceptibility to fragmentation or its resistance to thrombectomy devices. These characteristics are the ones that seem to be relevant in the, in many ways, opaque radiomic analysis. In terms of predicting the outcome of revascularization, radiomics can be useful in predicting the number of thrombectomy attempts required to achieve adequate reperfusion. It can also provide valuable information on the final outcome of thrombectomy by predicting the TICI scale.