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Open Access 01-12-2024 | Stroke | Research

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

Authors: 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

Published in: BMC Neurology | Issue 1/2024

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.

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Title: Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia
Authors: 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
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Stroke
Pneumonia
Published in: BMC Neurology / Issue 1/2024
Electronic ISSN: 1471-2377
DOI: https://doi.org/10.1186/s12883-024-03532-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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