White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction

Lin, Jixian; Li, Xutong; Wu, Guoqing; Chen, Xi; Weng, Yingfeng; Wang, Hao; Song, Bin; Yu, Jinhua; Zhao, Jing

doi:10.1159/000505807

Cited by 2 publications

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References 30 publications

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“…According to previous studies, NCCT-based radiomics features have superior abilities in various disciplines. Regarding stroke, several problems have been reported, such as prediction of hemorrhage expansion after spontaneous intracerebral hemorrhage [ 18 , 19 ], early identification of ischemic stroke [ 20 ] and estimating infarction onset time [ 21 ]. However, the method of obtaining radiomics features based on NCCT has not been utilized for the prediction of HT risk following thrombolysis, which is the primary focus of the present study.…”

Section: Introductionmentioning

confidence: 99%

A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

et al. 2023

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Objective To build a clinical–radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). Materials and methods A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical–radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC). Results Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873–0.921) in the internal validation cohort, and 0.911 (95% CI 0.891–0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896–0.941) and 0.883 (95% CI 0.851–0.902), while the AUC of clinical–radiomics model was 0.950 (95% CI 0.925–0.967) and 0.942 (95% CI 0.927–0.958) respectively. Conclusion The proposed clinical–radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.

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Section: Introductionmentioning

confidence: 99%

A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

et al. 2023

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“…Ischemic stroke can be visible on the magnetic resonance imaging (MRI) even within 3 min after the onset of symptoms [ 6 ], and diffusion-weighted imaging (DWI) is the gold standard for evaluating the extent of ischemic stroke [ 7 ]. However, MRI has limited availability, and cannot be promptly used in many emergent cases.…”

Section: Introductionmentioning

confidence: 99%

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion

Wen

Shu

et al. 2021

BMC Med Imaging

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Background Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). Methods A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. Results The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665–0.875) and 0.792 (95% CI: 0.633–0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701–0.916) and 0.833 (95% CI: 0.702–0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614–0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712–0.928). Conclusions The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

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White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction

Cited by 2 publications

References 30 publications

A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion

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