108985
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
开发并验证用于预测有症状颅内动脉狭窄患者长期预后的深度学习模型
Authors Ding Q, Zhang S , Pan L
Received 3 June 2025
Accepted for publication 18 September 2025
Published 24 October 2025 Volume 2025:18 Pages 6455—6465
DOI https://doi.org/10.2147/IJGM.S538889
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Redoy Ranjan
Qianjin Ding,1 Shaojie Zhang,2 Li Pan2
1Department of Neurosurgery, Xinxiang Center Hospital, Xinxiang, Henan, 453000, People’s Republic of China; 2Department of Neurosurgery, Yangtze River Shipping General Hospital, Wuhan, Hubei, 430014, People’s Republic of China
Correspondence: Li Pan, Department of Neurosurgery, Yangtze River Shipping General Hospital, No. 5 Huijiroad, Jiangan District, Wuhan, Hubei, 430014, People’s Republic of China, Email ppl800313@163.com Shaojie Zhang, Department of Neurosurgery, Yangtze River Shipping General Hospital, No. 5 Huijiroad, Jiangan District, Wuhan, Hubei, 430014, People’s Republic of China, Email 296921638@qq.com
Background and Aim: Symptomatic intracranial arterial stenosis (ICAS) is a leading cause of ischemic stroke, and its progression is associated with an increased risk of stroke recurrence and poor outcomes. Accurate prediction of the risk of progression in ICAS patients is crucial for timely intervention and management. This study aims to develop and validate logistic regression and deep learning models to predict the risk of progression in symptomatic ICAS patients and compare their predictive performance.
Methods: A retrospective study was conducted on 266 symptomatic ICAS patients who were followed for at least 3 years. The dataset was randomly split into a training set (70%) and a validation set (30%). Data preprocessing involved normalization, feature selection, and class balancing techniques to enhance model performance. Logistic regression, and deep learning models were developed to predict the risk of ICAS progression. The models were evaluated using accuracy, sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC).
Results: The logistic regression model achieved an AUC of 0.771 (training) and 0.767 (validation; 95% CI: 0.702– 0.832). The deep learning model demonstrated superior performance with an AUC of 0.898 (training) and 0.863 (validation; 95% CI: 0.801– 0.925), showing a statistically significant improvement (p = 0.016, DeLong’s test). Feature importance analysis identified hypertension, diabetes, stenosis degree, and prior stroke history as the most influential predictors of ICAS progression. These results highlight the value of early risk stratification to guide timely clinical intervention.
Conclusion: Compared to logistic regression, the deep learning model exhibited significantly improved predictive accuracy for the risk of progression in symptomatic ICAS patients. The high performance and reliability of the deep learning model highlight its potential clinical utility in predicting ICAS progression, ultimately aiding in risk stratification and personalized treatment strategies.
Keywords: symptomatic intracranial arterial stenosis, risk of progression, logistic regression, deep learning
1Department of Neurosurgery, Xinxiang Center Hospital, Xinxiang, Henan, 453000, People’s Republic of China; 2Department of Neurosurgery, Yangtze River Shipping General Hospital, Wuhan, Hubei, 430014, People’s Republic of China
Correspondence: Li Pan, Department of Neurosurgery, Yangtze River Shipping General Hospital, No. 5 Huijiroad, Jiangan District, Wuhan, Hubei, 430014, People’s Republic of China, Email ppl800313@163.com Shaojie Zhang, Department of Neurosurgery, Yangtze River Shipping General Hospital, No. 5 Huijiroad, Jiangan District, Wuhan, Hubei, 430014, People’s Republic of China, Email 296921638@qq.com
Background and Aim: Symptomatic intracranial arterial stenosis (ICAS) is a leading cause of ischemic stroke, and its progression is associated with an increased risk of stroke recurrence and poor outcomes. Accurate prediction of the risk of progression in ICAS patients is crucial for timely intervention and management. This study aims to develop and validate logistic regression and deep learning models to predict the risk of progression in symptomatic ICAS patients and compare their predictive performance.
Methods: A retrospective study was conducted on 266 symptomatic ICAS patients who were followed for at least 3 years. The dataset was randomly split into a training set (70%) and a validation set (30%). Data preprocessing involved normalization, feature selection, and class balancing techniques to enhance model performance. Logistic regression, and deep learning models were developed to predict the risk of ICAS progression. The models were evaluated using accuracy, sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC).
Results: The logistic regression model achieved an AUC of 0.771 (training) and 0.767 (validation; 95% CI: 0.702– 0.832). The deep learning model demonstrated superior performance with an AUC of 0.898 (training) and 0.863 (validation; 95% CI: 0.801– 0.925), showing a statistically significant improvement (p = 0.016, DeLong’s test). Feature importance analysis identified hypertension, diabetes, stenosis degree, and prior stroke history as the most influential predictors of ICAS progression. These results highlight the value of early risk stratification to guide timely clinical intervention.
Conclusion: Compared to logistic regression, the deep learning model exhibited significantly improved predictive accuracy for the risk of progression in symptomatic ICAS patients. The high performance and reliability of the deep learning model highlight its potential clinical utility in predicting ICAS progression, ultimately aiding in risk stratification and personalized treatment strategies.
Keywords: symptomatic intracranial arterial stenosis, risk of progression, logistic regression, deep learning