Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning

Wang, Xueyang; Lyu, Jinhao; Meng, Zhihua; Wu, Xiaoyan; Chen, Wen; Wang, Guohua; Niu, Qingliang; Li, Xin; Bian, Yitong; Han, Dan; Guo, Weiting; Yang, Shuai; Bian, Xiaowei; Lan, Yina; Wang, Liu-Xian; Duan, Qi; Zhang, Tingyang; Duan, Caohui; Tian, Chenglin; Chen, Ling; Lou, Xin

doi:10.1111/cns.14071

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…They found that among above mentioned markers, only myeloperoxidase levels were altered, and this element was considered an important feature in the detection and prediction of cSVD. Wang et al [ 104 ] assessed the prognostic ability of SVD imaging markers on acute ischemic stroke subtypes using machine learning and logistical regression methods and found that in lacunar stroke patients, models using SVD imaging markers could rapidly predict prognosis. Nevertheless, the mechanism by which SVD affects the prognosis of acute ischemic stroke patients is poorly understood.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

et al. 2023

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The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

et al. 2023

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“…As expected, radiomics performed well in predicting RTLI as it can detect microstructural changes in the temporal lobe early ( 11 ), enhancing prediction accuracy, as seen in the meta-analysis for each subgroup above. ML methods are required to process high-dimensional mineable data like radiomics, and these methods have been shown to play a crucial part in predictive models based on radiomics and standard clinical characteristics, such as predicting rapidly deteriorating mild cognitive impairment in Alzheimer’s disease ( 13 ) and the prognosis of acute ischemic stroke ( 12 ). LR is the most often used ML approach for building models due to its ease of use and consistently good performance.…”

Section: Discussionmentioning

confidence: 99%

“…However, previous models were constructed without radiomics due to the limitations of manual data processing in handling large volumes of complex data. An increasing number of promising studies have attempted to employ machine learning (ML) approaches to develop prediction models relying on radiomics ( 12 , 13 ). The biggest strength of this kind of model is the personalized prediction at the early stage of RTLI whereas the biggest weakness is the dependence on imaging data, which may lead to possible additional medical expenses.…”

Section: Introductionmentioning

confidence: 99%

Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis

Li,

Gong,

Guo

et al. 2023

Transl Cancer Res

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Background Radiotherapy is a common treatment for nasopharyngeal carcinoma (NPC) but can cause radiation-induced temporal lobe injury (RTLI), resulting in irreversible damage. Predicting RTLI at the early stage may help with that issue by personalized adjustment of radiation dose based on the predicted risk. Machine learning (ML) models have recently been used to predict RTLI but their predictive accuracy remains unclear because the reported concordance index (C-index) varied widely from around 0.31 to 0.97. Therefore, a meta-analysis was needed. Methods The PubMed, Web of Science, Embase, and Cochrane Library databases were searched from inception to November 2022. Studies that fully develop one or more ML risk models of RTLI after radiotherapy for NPC were included. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess the risk of bias in the included research. The primary outcome of this review was the C-index, specificity (Spe), and sensitivity (Sen). Results The meta-analysis included 14 studies with 15,573 NPC patients reporting a total of 72 prediction models. Overall, 94.44% of models were found to have a high risk of bias. Radiomics was included in 57 models, dosimetric predictors in 28, and clinical data in 27. The pooled C-index for ML models predicting RTLI was 0.77 [95% confidence interval (CI): 0.75–0.79] in the training set and 0.78 (95% CI: 0.75–0.81) in the validation set. The pooled Sen was 0.75 (95% CI: 0.69–0.80) in the training set and 0.70 (95% CI: 0.66–0.73) in the validation set and the pooled Spe was 0.78 (95% CI: 0.73–0.82) in the training set and 0.79 (95% CI: 0.75–0.82) in the validation set. Models with radiomics and clinical data achieved the most excellent discriminative performance, with a pooled C-index of 0.895. Conclusions ML models can accurately predict RTLI at an early stage, allowing for timely interventions to prevent further damage. The kind of ML methods and the selection of predictors may influence the predictive accuracy.

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“…Association between CSVD and stroke outcome has been studied extensively, but still inconclusive (7,(13)(14)(15). While its predictive value in early neurological deterioration has not been comprehensively investigated.…”

Section: Csvd and End In Isolated Pontine Infarctionmentioning

confidence: 99%

Association between cerebral small vessel disease burden and early neurological deterioration in isolated pontine infarction

song,

Hou,

Zhao

et al. 2024

Preprint

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Objective To investigate the association between cerebral small vessel disease burden and early neurological deterioration (END) in stroke patients with isolated pontine infarction. Methods A total of 107 acute isolated pontine infarct patients within 24 hours of symptoms onset were included from a comprehensive stroke center, the mean age is 67 years old. Cerebral small vessel disease burden on brain MRI including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), and enlarged perivascular spaces (EPVS) were evaluated in each patient. END was defined as an increase of ≥ 1 point on the motor National Institutes of Health Stroke Scale (NIHSS) or ≥ 2 points on the total NIHSS score within 72 hours from admission. T-test, chi-square test, and logistic regression were used for statistical analysis. Results 33.6% (36/107) of them occurred END. Patients in the END group have a higher percentage of hyperlipidemia (66.7% vs. 43.7%, p = 0.024). over 50% of infarction mechanism can be attributed to basilar artery branch disease in both groups (58.3% in the END group and 50.7% in the non-END group). In multivariate regression, neither cerebral small vessel disease total burden nor WMH, CMB, lacunes, and EPVS were associated with END (all P >0.05). Basilar artery intraplaque hemorrhage on vessel wall imaging is associated with END independently (OR = 3.233, 95% CI 1.032–10.123, p = 0.044). Conclusion Neither cerebral small vessel disease burden nor its individual imaging features were associated with END in isolated pontine infarction patients. Basilar artery intraplaque hemorrhage is an independent predictor of END in this population.

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Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning

Cited by 11 publications

References 34 publications

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

Predictive accuracy of machine learning for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients: a systematic review and meta-analysis

Association between cerebral small vessel disease burden and early neurological deterioration in isolated pontine infarction

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