CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time

Yao, Xiang; Mao, Ling; Lv, Shunli; Ren, Zhiguo; Li, Wentao; Kawaguchi, Ren

doi:10.1016/j.jns.2020.116730

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…CT radiomics based on texture features showed good performance in identifying hyperacute or AIS lesions, assessing the extent of ischemic lesions, and determining the time from symptoms onset in basal ganglia infarction. 24 , 25 , 26 Kuang et al 27 developed an automated method to compute the ASPECTS on NCCT images from patients with AIS. Additionally, texture analysis based on MRI effectively identified the presence of ischemic stroke lesions and detected hemorrhagic transformation in patients with AIS.…”

Section: Discussionmentioning

confidence: 99%

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

Wen¹,

Hu²,

Xiao³

et al. 2023

dir

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T he safety and efficacy of endovascular treatment (EVT) for anterior circulation large vessel occlusive stroke (LVOS) have been verified in randomized controlled trials. However, nearly 45% of the cases with LVOS cannot recover after EVT. 1,2 Recent evidence has revealed that malignant cerebral edema (MCE) commonly occurs after EVT, and the development of MCE may reduce the benefit-risk ratio of EVT. [3][4][5] MCE is a severe complication of stroke that occurs in approximately 10% of all patients with stroke. The prognosis for MCE is poor, with a mortality rate of 80% when treated conservatively. 6 Early decompressive surgery effectively lowers mortality and improves the clinical outcomes of patients with MCE. 7 The addition of decompressive craniectomy to the best medical therapy can reduce the mortality rate of MCE by 50%-75%. 8 Hence, the early identification of patients at a high risk of MCE is critical for therapeutic decision-making. The imaging predictors for MCE that have been studied so far primarily involve the characterization of the infarction size, neurovascular condition, and brain perfusion of the patient. 7 PURPOSERadiomics analysis is a promising image analysis technique. This study aims to extract a radiomics signature from baseline computed tomography (CT) to predict malignant cerebral edema (MCE) in patients with acute anterior circulation infarction after endovascular treatment (EVT). METHODSIn this retrospective study, 111 patients underwent EVT for acute ischemic stroke caused by middle cerebral artery (MCA) and/or internal carotid artery occlusion. The participants were randomly divided into two datasets: the training set (n = 77) and the test set (n = 34). The clinico-radiological profiles of all patients were collected, including cranial non-contrast-enhanced CT, CT angiography, and CT perfusion. The MCA territory on non-contrast-enhanced CT images was segmented, and the radiomics features associated with MCE were analyzed. The clinico-radiological parameters related to MCE were also identified. In addition, a routine visual radiological model based on radiological factors and a combined model comprising radiomics features and clinico-radiological factors were constructed to predict MCE. RESULTSThe areas under the curve (AUCs) of the radiomics signature for predicting MCE were 0.870 (P < 0.001) and 0.837 (P = 0.002) in the training and test sets, respectively. The AUCs of the routine visual radiological model were 0.808 (P < 0.001) and 0.813 (P = 0.005) in the training and test sets, respectively. The AUCs of the model combining the radiomics signature and clinico-radiological factors were 0.924 (P < 0.001) and 0.879 (P = 0.001) in the training and test sets, respectively. CONCLUSIONA CT image-based radiomics signature is a promising tool for predicting MCE in patients with acute anterior circulation infarction after EVT. For clinicians, it may assist in diagnostic decision-making.

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Section: Discussionmentioning

confidence: 99%

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

Wen¹,

Hu²,

Xiao³

et al. 2023

dir

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“…However, few studies have focused on the radiomics for discriminating the onset time of stroke. Yao et al [ 24 ] constructed a CT-based radiomics signature for determining the onset time of symptoms in patients with basal ganglia infarction, and the radiomics signature exhibited a satisfying performance in both the development and validation cohorts. Furthermore, they also proposed that the radiomics signature may assist in therapeutic options.…”

Section: Discussionmentioning

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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“…The sensitivity and specificity of the validation set for the value of applying machine learning in predicting the time of symptom onset in stroke patients. Some studies used multiple different types of machine learning models; therefore, some studies are presented multiple times in the figure [17,18,[24][25][26][27][28][29][30][31][32][33][34].…”

Section: Sensitivity and Specificitymentioning

confidence: 99%

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

Feng,

Zhang,

et al. 2023

J Med Internet Res

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Background Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. Objective We aimed to assess the value of applying machine learning in predicting the time of stroke onset. Methods PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). Results Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). Conclusions Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. Trial Registration PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898

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CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time

Cited by 13 publications

References 30 publications

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

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

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

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