Computerized Tomography Radiomics Features Analysis for Evaluation of Perihematomal Edema in Basal Ganglia Hemorrhage

Yao, Xiang; Liao, Lishang; Han, Yuxiao; Wei, Ting; Wu, Huifang; Wang, Yiying; Li, Yanfei; Zhang, Mengjie; Kawaguchi, Ren

doi:10.1097/scs.0000000000005765

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…In line with our results, the GCS score has been shown to be strongly associated with the outcome of basal ganglia hemorrhage patients and is an independent predictor of critical care ( 32 , 33 ). Similar to the previous studies, plasma DD2 could predict poor outcome and mortality in ICH patients ( 34 – 36 ). We used these four risk factors to build a clinical model using logistic regression and yielded in the test cohort (AUC: 0.85), which indicated that the clinical model does not provide sufficient accuracy for predicting prognosis in basal ganglia hemorrhage patients.…”

Section: Discussionsupporting

confidence: 86%

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Zhou

Sun²,

Song³

et al. 2022

Front. Neurol.

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ObjectiveWe developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients.MethodsRetrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort.ResultsThe clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts.ConclusionRadiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.

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Section: Discussionsupporting

confidence: 86%

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Zhou

Sun²,

Song³

et al. 2022

Front. Neurol.

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“…HR VW-MRI provides useful information to distinguish different types of aneurysms [14,22,23], and this is the first time using the machine-learning method of multicenter external verification to distinguish DA from hemorrhagic SA [24]. Unlike several previous studies, that only draw ROI from a single image slice [25], the ROIs in this study are drawn in all slices with a thickness of only 1.0 mm, which contains sufficient data information. ElasticNet, as a compression estimation method for variable selection, is suitable for the selection of biomarkers in high-dimensional data [20,26,27].…”

Section: Discussionmentioning

confidence: 99%

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study

Cao

Zeng

Wang

et al. 2022

JCM

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The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.

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“…CTA was used in this study as it is the most frequently employed imaging modality for aneurysm evaluation. Previous studies only assessed the region of interest from a single image slice ( 32 ), while we delineated the region of interest from all image slices with a slice thickness of 0.75 mm, and extracted three-dimensional aneurysm volumes as the basis of radiomic and morphological characteristic analysis.…”

Section: Discussionmentioning

confidence: 99%

Construction and Evaluation of Multiple Radiomics Models for Identifying the Instability of Intracranial Aneurysms Based on CTA

Zhou

Chen

et al. 2022

Front. Neurol.

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Background and AimsIdentifying unruptured intracranial aneurysm instability is crucial for therapeutic decision-making. This study aims to evaluate the role of Radiomics and traditional morphological features in identifying aneurysm instability by constructing and comparing multiple models.Materials and MethodsA total of 227 patients with 254 intracranial aneurysms evaluated by CTA were included. Aneurysms were divided into unstable and stable groups using comprehensive criteria: the unstable group was defined as aneurysms with near-term rupture, growth during follow-up, or caused compressive symptoms; those without the aforementioned conditions were grouped as stable aneurysms. Aneurysms were randomly divided into training and test sets at a 1:1 ratio. Radiomics and traditional morphological features (maximum diameter, irregular shape, aspect ratio, size ratio, location, etc.) were extracted. Three basic models and two integrated models were constructed after corresponding statistical analysis. Model A used traditional morphological parameters. Model B used Radiomics features. Model C used the Radiomics features related to aneurysm morphology. Furthermore, integrated models of traditional and Radiomics features were built (model A+B, model A+C). The area under curves (AUC) of each model was calculated and compared.ResultsThere were 31 (13.7%) patients harboring 36 (14.2%) unstable aneurysms, 15 of which ruptured post-imaging, 16 with growth on serial imaging, and 5 with compressive symptoms, respectively. Four traditional morphological features, six Radiomics features, and three Radiomics-derived morphological features were identified. The classification of aneurysm stability was as follows: the AUC of the training set and test set in models A, B, and C are 0.888 (95% CI 0.808–0.967) and 0.818 (95% CI 0.705–0.932), 0.865 (95% CI 0.777–0.952) and 0.739 (95% CI 0.636–0.841), 0.605(95% CI 0.470–0.740) and 0.552 (95% CI 0.401–0.703), respectively. The AUC of integrated Model A+B was numerically slightly higher than any single model, whereas Model A+C was not.ConclusionsA radiomics and traditional morphology integrated model seems to be an effective tool for identifying intracranial aneurysm instability, whereas the use of Radiomics-derived morphological features alone is not recommended. Radiomics-based models were not superior to the traditional morphological features model.

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Computerized Tomography Radiomics Features Analysis for Evaluation of Perihematomal Edema in Basal Ganglia Hemorrhage

Cited by 10 publications

References 20 publications

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study

Construction and Evaluation of Multiple Radiomics Models for Identifying the Instability of Intracranial Aneurysms Based on CTA

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