North University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, China.
College of Big Data, North University of China, 3 Xueyuan Road, Taiyuan, Shanxi 030051, China.
Biomed Res Int. 2020 Nov 15;2020:8864756. doi: 10.1155/2020/8864756. eCollection 2020.
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.
本研究旨在分析 CT 不显影的急性脑梗死病灶的可分离性。共纳入 38 例经 CT 和 MRI 诊断为急性脑梗死的患者和 18 例 CT 和 MRI 均无阳性发现的患者。对病灶及对称区、正常脑及对称区、病灶和正常脑区进行对比研究。对 CT 进行 MRI 重建和仿射变换，以获得准确的病灶位置。引入放射组学特征和信息增益，以捕获有效特征。最后，使用选定的特征建立 10 个分类器来评估分析的有效性。经过配准后，从候选区域提取了 1301 个放射组学特征。对于病灶及其对称区，有 280 个特征的信息增益大于 0.1，有 2 个特征的信息增益大于 0.3。平均分类准确率为 0.6467，最佳分类准确率为 0.7748。对于正常脑及其对称区，有 176 个特征的信息增益大于 0.1，有 1 个特征的信息增益大于 0.2。平均分类准确率为 0.5414，最佳分类准确率为 0.6782。对于正常脑和病灶，有 501 个特征的信息增益大于 0.1，有 1 个特征的信息增益大于 0.5。平均分类准确率为 0.7480，最佳分类准确率为 0.8694。综上所述，该研究捕捉到与急性脑梗死相关的显著特征，并证实了 CT 中急性病灶的可分离性，为进一步的人工智能辅助 CT 诊断奠定了基础。
