Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke

Abstract

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA). This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA,13 feature sets (Fmethod) were obtained from different feature selection algorithms. Furthermore, these 13 Fmethod were validated in identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue. In identifying HA and NA, the composite score (CS) of the 13 Fmethod ranged from 0.624 to 0.925. FLasso in the 13 Fmethod achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. The classification ability was relatively stable when the reference threshold (RT) was <0.25. Otherwise, when RT was >0.25, the performance will gradually decrease as its increases. These results showed that radiomics features extracted from the Lasso algorithms could accurately reflect cerebral blood flow changes and classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithms in the future.

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

“…Other studies of some prognostic predictions have found similar results [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In these studies, we can see that the performance of radiomics based on different types of images (MRI, CT and CTA) gives good results in predicting prognostic factors such as the mRS scale after AIS or the presence of disability after AIS.…”

The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview

“…By measuring the first pass of contrast, PWI provides quantitative information about CBF, cerebral blood volume (CBV), and mean transit time (MTT). PWI is beneficial in the evaluation of acute ischemic strokes as well ( 33 ). It can distinguish between regions of hypoperfusion and regions of completed infarction, aiding in the selection of patients who may benefit from reperfusion therapies.…”

Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

“…The training dataset consisted of 159 LGG patients with pre-operative MRI images and 1p/19q status proven by biopsy. They were identified within the LGG-1p19q Deletion dataset [15,26,27] on The Cancer Imaging Archive (TCIA).…”

“…In [158], the deep learning model was trained to detect the stroke area on non-contrast CT scans. In [159], the model was trained to distinguish between hyperperfusion areas from normal ones. In [160], primary and secondary hemorrhages were classified.…”