Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia
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Published:2024-01-25
Issue:1
Volume:24
Page:
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ISSN:1471-2377
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Container-title:BMC Neurology
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language:en
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Short-container-title:BMC Neurol
Author:
Luo Haowen,Li Jingyi,Chen Yongsen,Wu Bin,Liu Jianmo,Han Mengqi,Wu Yifan,Jia Weijie,Yu Pengfei,Cheng Rui,Wang Xiaoman,Ke Jingyao,Xian Hongfei,Tu Jianglong,Yi Yingping
Abstract
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 < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759–0.936) and validation (AUC = 0.830, 95% CI 0.758–0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability.
Conclusion
The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.
Funder
the National Natural Science Foundation of China the Second Affiliated Hospital of Nanchang University Funding Program Jiangxi Provincial Key R&D Plan the National Key R&D Program of China the Applied Research Cultivation Program of Jiangxi Province
Publisher
Springer Science and Business Media LLC
Subject
Neurology (clinical),General Medicine
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