Background: We developed an image patch classification-based method to detect early ischemic stroke. The accuracy of this method was >75%. We aimed to analyze patients' image data to identify interference factors that would affect its accuracy.
Methods: We conducted a retrospective analysis of 162 patients who were hospitalized with acute ischemic stroke. Factors related to the noncontrast computed tomography (ncCT) determination results were analyzed according to the patient's sex, age, clinical symptoms, cerebral infarction volume, cerebral infarction location, and whether or not the white matter high (WMH) signal was combined.
Results: The volume of cerebral infarction was positively correlated with the predicted results. The correct percentages of patients with volumes >1 and <1 mL were 59.18 and 83.19%, respectively, and the difference was statistically significant (p = 0.001). The correct percentage of the internal capsule region (47.1%) was significantly lower than that of the other groups (p = 0.011). The correct percentage of lateral ventricular paraventricular infarction was significantly lower than that of non-lateral ventricle patients (70.8 vs. 85.7%). In patients with lateral ventricular paraventricular infarction, if the WMH was combined, the correct percentage will decreased further as the Fazekas level increased. The correct percentage of lateral ventricle infarction combined with Fazekas 3 was 40.0%, which was statistically significant compared with the patient having Fazekas 0 with lateral ventricular infarction (p = 0.01).
Conclusions: WMH had a similar computed tomography appearance to cerebral infarction and could interfere with the prediction of the cerebral infarction region by ncCT. This result provides a reference for clinicians to choose imaging methods for identifying acute cerebral infarction areas.
Keywords: Early identification; Ischemic stroke; Magnetic resonance imaging; Noncontrast computed tomography; Radiomics; White matter disease.
© 2020 S. Karger AG, Basel.