# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Treatment Predictions  
Regarding radiomic models predicting the outcome of reperfusion treatments, most studies focus on predicting the first-pass effect or the TICI scale after thrombectomy. Only one study focuses on predicting recanalization after atelphase treatment.  
To predict first-pass recanalization, Hofmeister et al. performed clot segmentation in their study of 156 patients. They used RFs to identify patients with first-pass recanalization by aspiration. They also predicted the total number of passes required to achieve good recanalization with a mechanical thrombectomy device (MTB). They achieved an AUC of 0.88 for predicting patients with first attempt recanalization. They concluded that clot-based radiomics can predict an MTB strategy in patients with AIS [43]. Sarioglu et al.  
attempted to predict first attempt recanalization in AIS patients using clot-based radiomics features obtained from NCECT. They achieved an accuracy of 0.83 using a combined model with radiomic features and clinical data (ASPECTS scale and gender). They also concluded that clot-based radiomic features can help to estimate successful recanalization in AIS patients [44]. Patel et al. also developed a NCECT/CTA-based radiomic model to predict the first-pass effect in AIS patients with similar results (AUC of 0.832) [45].  
In recanalization prediction, Zhang et al. attempted to predict successful recanalization (TICI \(\geq\) 2c) using MRI-based radiomics in 141 AIS patients. They extracted 321 radiomic features from the ischemic lesion and performed a manual segmentation. They obtained an AUC of 0.7442, a sensitivity of 0.7556 and a specificity of 0.7675. They concluded that MRI-based radiomics may be useful in predicting patient response to thrombectomy [46]. Xiong et al. also attempted to predict successful recanalization (TICI \(\geq\) 2b) in AIS patients undergoing stent retrieval. They improved the results of Zhang et al. with an AUC of 0.860 and 0.849 in the training and validation cohorts, respectively, with the combined model. They also conclude that the radiomics model can successfully predict recanalization after stent retrieval [47]. Finally, Van Voorst et al. developed a clot-based radiomics model using the CT images of 699 patients to predict TICI \(\geq\) 2b, first attempt reperfusion, reperfusion within three attempts and mRS scale \(\leq\) 2. They obtained similar results with the clinical, radiomics and morphological features models [48].  
In terms of predicting recanalization after treatment with altephase, Qiu et al. obtained an AUC of 0.85. They concluded that clot-based radiomics was more predictive of recanalization with altephase than other classic thrombus imaging features (length, volume and permeability) [49].  
As we have seen in this section, the performance of radiomic models in predicting the outcome of thrombectomy in patients with AIS is very good. In this situation, radiomics can provide valuable information for the immediate management of these patients, since knowing in advance the risk of achieving inadequate reperfusion with a specific thrombectomy technique may lead the neuroradiologist to change the recanalization strategy in the event of an unsuccessful first pass.