Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis
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 st...
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doaj-91e9fe626e544961b6f6d211b63082fd2020-11-30T09:11:22ZengHindawi LimitedBioMed Research International2314-61332314-61412020-01-01202010.1155/2020/88647568864756Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted DiagnosisYun Guan0Peng Wang1Qi Wang2Peihao Li3Jianchao Zeng4Pinle Qin5Yanfeng Meng6North University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaNorth University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaNorth University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaSchool of Information and Communication Engineering, North University of China, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaNorth University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaNorth University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaNorth University of China-Taiyuan Central Hospital Joint Innovation Institute, 3 Xueyuan Road, Taiyuan, Shanxi 030051, ChinaThis 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.http://dx.doi.org/10.1155/2020/8864756 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yun Guan Peng Wang Qi Wang Peihao Li Jianchao Zeng Pinle Qin Yanfeng Meng |
spellingShingle |
Yun Guan Peng Wang Qi Wang Peihao Li Jianchao Zeng Pinle Qin Yanfeng Meng Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis BioMed Research International |
author_facet |
Yun Guan Peng Wang Qi Wang Peihao Li Jianchao Zeng Pinle Qin Yanfeng Meng |
author_sort |
Yun Guan |
title |
Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis |
title_short |
Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis |
title_full |
Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis |
title_fullStr |
Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis |
title_full_unstemmed |
Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis |
title_sort |
separability of acute cerebral infarction lesions in ct based radiomics: toward artificial intelligence-assisted diagnosis |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/8864756 |
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