# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Detection of Ischemic Stroke  
In the detection of ischemic stroke, articles focus on the early detection of ischemic lesions and the prediction of recurrence. The group of Guan Y et al. showed in their study with 56 patients and 1301 RFs that there is a correlation between RFs and AIS detection (best Acc of 0.7748) [36]. Guo Y et al. also investigated the detection of ischemic stroke (IS) and the prediction of NIHSS and other outcomes using RFs obtained from DSC-PWI in 88 patients. The best AUC they obtained was 0.925 for stroke detection. For the prediction of NIHSS and other prognostic predictions, they obtained an AUC of 0.853 and 0.828, respectively [37]. Zhang et al. also tried to predict early brainstem infarction using an NCECT-based radiomics model. They obtained an AUC of 0.99 and 0.91 in the internal and external validation cohorts, respectively. They concluded that NCECT-based radiomics can detect early brainstem infarction and may be very useful in clinical practice [38].  
On the other hand, Zhang R et al. used MRI-based radiomics to predict the ischemic penumbra in their study of 241 patients. They extracted 896 radiomic features from the DWI high signal area using manual segmentation. The AUC obtained was 0.92, and sensitivity, specificity and accuracy were 0.93, 0.75 and 0.82, respectively, in the training cohort. In the validation cohort the AUC, sensitivity, specificity and accuracy were 0.90, 0.88, 0.74 and 0.80, respectively. They concluded that a radiomics model based on MRI images can identify the ischemic penumbra in AIS patients [39].  
In ischemic stroke recurrence prediction, Su et al. developed a combined model with some clinical factors (age, sex, number of chronic ischemic lesions, routine laboratory tests, carotid ultrasound, cardiovascular risk factors and medical treatment) and CT-based radiomics to predict future ischemic stroke in patients with silent lacunar infarction. They obtained a C-index of 0.786 and 0.714 in the training and validation cohorts, respectively. They concluded that radiomic features can provide important information for predicting future ischemic stroke in patients with silent lacunar infarction [40]. Tang et al. also developed a radiomics signature based on MRI features of patients with symptomatic intracranial atherosclerotic stenosis (SICAS) to predict stroke recurrence. They obtained an AUC with the combined model of 0.899 and 0.803 in the training and validation cohorts, respectively. They concluded that radiomic features are useful for predicting stroke recurrence in patients with SICAS [41]. Wang et al. also predicted 1-year ischemic stroke recurrence in 1003 AIS patients based on MRI images. They obtained similar results [42].  
Many patients without vessel occlusion on CTA images or with inconclusive results on perfusion images require MRI imaging to determine the presence of ischemic lesions. In many cases, MRI images are not available in the first hours of management of these patients. In these situations, radiomics could provide valuable information for the early management of these patients.