Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia

Autor: Haowen Luo, Jingyi Li, Yongsen Chen, Bin Wu, Jianmo Liu, Mengqi Han, Yifan Wu, Weijie Jia, Pengfei Yu, Rui Cheng, Xiaoman Wang, Jingyao Ke, Hongfei Xian, Jianglong Tu, Yingping Yi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Neurology, Vol 24, Iss 1, Pp 1-15 (2024)
Druh dokumentu: article
ISSN: 1471-2377
DOI: 10.1186/s12883-024-03532-3
Popis: Abstract Purpose To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. Methods Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. Results Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P
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