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Luo H, Li J, Chen Y, Wu B, Liu J, Han M, Wu Y, Jia W, Yu P, Cheng R, Wang X, Ke J, Xian H, Tu J, Yi Y. Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia. BMC Neurol 2024; 24:45. [PMID: 38273251 PMCID: PMC10809767 DOI: 10.1186/s12883-024-03532-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/08/2024] [Indexed: 01/27/2024] Open
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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Affiliation(s)
- Haowen Luo
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Jingyi Li
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Yongsen Chen
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Bin Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jianmo Liu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Mengqi Han
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Yifan Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Weijie Jia
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Pengfei Yu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Rui Cheng
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Xiaoman Wang
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jingyao Ke
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Hongfei Xian
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jianglong Tu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China.
| | - Yingping Yi
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China.
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Li J, Han M, Chen Y, Wu B, Wu Y, Jia W, Liu J, Luo H, Yu P, Tu J, Kuang J, Yi Y. Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China. BMJ Open 2023; 13:e076406. [PMID: 37816554 PMCID: PMC10565242 DOI: 10.1136/bmjopen-2023-076406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/20/2023] [Indexed: 10/12/2023] Open
Abstract
INTRODUCTION Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS). METHODS AND ANALYSIS A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated. ETHICS AND DISSEMINATION This study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences. TRIAL REGISTRATION NUMBER ChiCTR2200055209.
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Affiliation(s)
- Jingyi Li
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Mengqi Han
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Yongsen Chen
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Bin Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Yifan Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Weijie Jia
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - JianMo Liu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Haowen Luo
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Pengfei Yu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jianglong Tu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jie Kuang
- Jiangxi Province Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi, China
| | - Yingping Yi
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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Xin Y, Zhang J, Jiang Q, Qiu J. Construction of prognostic signature of patients with oral squamous cell carcinoma based on pyroptosis-related long non-coding RNAs. Front Surg 2022; 9:935765. [PMID: 36211292 PMCID: PMC9533653 DOI: 10.3389/fsurg.2022.935765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/08/2022] [Indexed: 01/05/2023] Open
Abstract
Background and objectiveOral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.MethodsThe RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.ResultsEight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.ConclusionThe prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.
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Affiliation(s)
- Yuqi Xin
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical College, Nanchang University, Nanchang, China
| | - Jieyuan Zhang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical College, Nanchang University, Nanchang, China
| | - Qingkun Jiang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Medical College, Nanchang University, Nanchang, China
| | - Jiaxuan Qiu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Correspondence: Jiaxuan Qiu
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