Abstract
This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled. Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions. MRI was reconstructed and affine transformed to obtain accurate lesion position of CT. Radiomic features and information gain were introduced to capture efficient features. Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis. 1301 radiomic features were extracted from candidate regions after registration. For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3. The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748. For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2. The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782. For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5. The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694. In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence-assisted CT diagnosis.
Highlights
Stroke is still the leading cause of mortality and disability, and there are substantial economic costs for poststroke care [1,2,3,4]
CT is very sensitive in detecting intracranial hemorrhagic stroke and chronic ischemic stroke
Between February 2019 and February 2020, we retrospectively studied 38 patients, who have performed both CT and MRI and diagnosed as acute cerebral infarction (ACI) by diffusion-weighted imaging (DWI); CT has no positive finding by radiologist
Summary
Stroke is still the leading cause of mortality and disability, and there are substantial economic costs for poststroke care [1,2,3,4]. CT is the preferred radiologic modality for patients with stroke-like clinical manifestation, since it is immediately available, cost effective, and capable of differentiating brain disorders [5]. CT is very sensitive in detecting intracranial hemorrhagic stroke and chronic ischemic stroke. CT detects acute cerebral infarction (ACI) in terms of decrease of CT attenuation, loss of gray-white matter differentiation, sulcal effacement, and other indirect signs [6]. Radiologists often encounter poor accuracy in diagnosing acute infarct by CT, with accuracy rate ≤67% within 3 hours [5]. Patient, who has stroke-like symptom but CT showed negative findings, needs MRI [7]. MR diffusion-weighted imaging (DWI) can detect ischemic lesions within minutes
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