Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia
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 an...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Neurology |
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| Online Access: | https://doi.org/10.1186/s12883-024-03532-3 |
| _version_ | 1797276501043838976 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Haowen Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T15:29:04Z |
| format | Article |
| id | doaj.art-c92c7dad92374a30afd2e98d0314aa17 |
| institution | Directory Open Access Journal |
| issn | 1471-2377 |
| language | English |
| last_indexed | 2024-03-07T15:29:04Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neurology |
| spelling | doaj.art-c92c7dad92374a30afd2e98d0314aa172024-03-05T16:33:29ZengBMCBMC Neurology1471-23772024-01-0124111510.1186/s12883-024-03532-3Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumoniaHaowen Luo0Jingyi Li1Yongsen Chen2Bin Wu3Jianmo Liu4Mengqi Han5Yifan Wu6Weijie Jia7Pengfei Yu8Rui Cheng9Xiaoman Wang10Jingyao Ke11Hongfei Xian12Jianglong Tu13Yingping Yi14Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityDepartment of Neurology, The Second Affiliated Hospital of Nanchang UniversityDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang UniversityAbstract 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.https://doi.org/10.1186/s12883-024-03532-3Stroke-associated pneumoniaRadiomicsPredictionAcute ischemic strokeMagnetic resonance imaging |
| spellingShingle | 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 Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia BMC Neurology Stroke-associated pneumonia Radiomics Prediction Acute ischemic stroke Magnetic resonance imaging |
| title | Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia |
| title_full | Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia |
| title_fullStr | Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia |
| title_full_unstemmed | Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia |
| title_short | Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia |
| title_sort | feasibility of a clinical radiomics combined model to predict the occurrence of stroke associated pneumonia |
| topic | Stroke-associated pneumonia Radiomics Prediction Acute ischemic stroke Magnetic resonance imaging |
| url | https://doi.org/10.1186/s12883-024-03532-3 |
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