Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging

Cao, Xin; Wei, Xia; Tang, Ye; Zhang, Bo; Yang, Jinming; Zeng, Yi; Geng, Daoying; Zhang, Jun

doi:10.1016/j.jstrokecerebrovasdis.2020.105268

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…10 At the same time, the imaging omics features based on HR-MRI can be used to establish an imaging omics model reliably that can effectively distinguish DA (Dissecting Aneurysm) from complex SA (Saccular Aneurysm). 11 There are some deficiencies in this study. First of all, fewer experimental cases were enrolled.…”

Section: Discussionmentioning

confidence: 95%

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms

Kong¹,

Li²,

Lv³

et al. 2023

IJGM

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To develop a radiomics nomogram model based on time-of-flight magnetic resonance angiography (TOF-MRA) images for preoperative prediction of true microaneurysms. Methods: 118 patients with Intracranial Aneurysm Sac (40 positive and 78 negative) were enrolled and allocated to training and validation groups (8:2 ratio). Findings of clinical characteristics and MRA features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training group. The radiomics nomogram model was constructed by combining clinical risk factors and radiomics signature. In order to compare the classification performance of clinical models, radiomics model and radiomics nomogram model, AUC was used to evaluate them. The performance of the radiomics nomogram model was evaluated by calibration curve and decision curve analysis. Results: Eleven features were selected to develop radiomics model with AUC of 0.875 (95% CI 0.78-0.97), sensitivity of 0.84, and specificity of 0.68. The radiomics model achieved a better diagnostic performance than the clinic model (AUC = 0.75, 95% CI: 0.53-0.97) and even radiologists. The radiomics nomogram model, which combines radiomics signature and clinical risk factors, is effective too (AUC = 0.913, 95% CI: 0.87-0.96). Furthermore, the decision curve analysis demonstrated significantly better net benefit in the radiomics nomogram model. Conclusion:Radiomics features derived from TOF-MRA can reliably be used to build a radiomics nomogram model for effectively differentiating between pseudo microaneurysms and true microaneurysms, and it can provide an objective basis for the selection of clinical treatment plans.

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Section: Discussionmentioning

confidence: 95%

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms

Kong¹,

Li²,

Lv³

et al. 2023

IJGM

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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.…”

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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Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging

Cited by 2 publications

References 39 publications

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms

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

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