## 6 Discussion  
Early ischemic signs (EISs) on NCCT scans are well-established subtle changes associated with early cerebral ischemia within 6 h of stroke onset [29]. The sensitivity of NCCT scans for detecting ischemic stroke varies with different time windows from disease onset, the vascular territory of the infarct, and the experience of the interpreter [29]. When ischemia occurs, cytotoxic edema begins to develop within the affected brain tissues which results in decreased NCCT attenuation. [PERSON] et al. [30] reported a decrease of 1.3 Hounsfield units (HU) at 2.5 h after symptom onset in regions with ischemia. However, a difference of 2 HU is the threshold that can be detected by the human eye [31]. Therefore, according to [PERSON]'s study, the overall sensitivity in the first 12 h after stroke onset is only around 32-52% [32; 33]. The highest detection rate was reached in patients with a total anterior circulation infarct (52%). Edema always progresses with time, and at 48 h after stroke onset, the second NCCT scan was positive in 85% of patients [32]. Since benefits of the latest treatment with an endovascular thrombectomy plus medical therapy related to lower degrees of disability at 3 months was proven to become non-significant at 7.3 h after disease onset [9], timely recognition of EISs is very critical for improving stroke outcomes. In the experiment, all NCCT images in the stroke group were obtained within 4 h after symptom onset. The average time from disease onset to NCCT acquisition was 74.5 \(\pm\) 63.8 min. On Ranklet-transformed NCCT images, textural features revealing relative local patterns were extracted to diagnose hyperacute ischemic stroke. Results showed that the prediction model based on the proposed CAD system achieved an 81% accuracy in distinguishing strokes and normal tissues. The performances of Ranklet features were better than those of conventional GLCM features in terms of accuracy, specificity, and Az (area under ROC curve) with respective \(p\) values of 0.0478, 0.0322, and 0.028. The local contrast enhanced by Ranklet transformation may provide additional diagnostic information. Figure 5 shows tissues from two patients with hyperacute ischemic stroke successfully classified using Ranklet features with respective likelihoods of 80% and 87%. The demonstrated cases had a homogeneity value of 0.04 in the horizontal orientation. Homogeneity measures differences or similarities between tissue intensities; when image contrast is weaker, homogeneity is greater. As for  
Figure 5: Two hyperacute stroke cases correctly classified by Ranklet features but misclassified by gray level co-occurrence matrix (GLCM) features. (**a**) A case of right middle cerebral artery (MCA) territory infarction with a stroke likelihood of 80%. (**b**) Another case of right MCA territory infarction with a stroke likelihood of 87%. the entropy feature, stroke cases with higher entropy have a lower level of complexity. As to the biological meaning of this, tissues suffering from ischemia may develop edema and become similar to each other. Our study used the more-informative Ranklet transformation to detect hyperacute ischemic stroke and achieved a more-significant performance than conventional GLCM features. In addition to the improved accuracy, the CAD system efficiently generated a diagnostic assessment which could be appropriately applied to hyperacute disease. Using a radiomic model for predicting hyperacute ischemic stroke provides a connection between intuitive vision and precision medicine. Hyperacute tissue damage can be mapped and quantified by applying high-throughput radiomic analyses of routine NCCT examinations obtained at a very early stage of the disease, without requiring more time-consuming examinations. However, this preliminary study was limited by the small number of hyperacute stroke cases. More cases should be collected in future studies to support the above results.