Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Emergency Radiology Pub Date : 2025-06-01 Epub Date: 2025-03-26 DOI:10.1007/s10140-025-02336-3
Mohsen Salimi, Pouria Vadipour, Amir Reza Bahadori, Shakiba Houshi, Ali Mirshamsi, Hossein Fatemian
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引用次数: 0

Abstract

Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transformation (HT) as a severe complication. Accurate prediction of HT is essential for optimizing treatment strategies. This review assesses the accuracy and utility of deep learning (DL) and radiomics in predicting HT through imaging, regarding clinical decision-making for AIS patients. A literature search was conducted across five databases (Pubmed, Scopus, Web of Science, Embase, IEEE) up to January 23, 2025. Studies involving DL or radiomics-based ML models for predicting HT in AIS patients were included. Data from training, validation, and clinical-combined models were extracted and analyzed separately. Pooled sensitivity, specificity, and AUC were calculated with a random-effects bivariate model. For the quality assessment of studies, the Methodological Radiomics Score (METRICS) and QUADAS-2 tool were used. 16 studies consisting of 3,083 individual participants were included in the meta-analysis. The pooled AUC for training cohorts was 0.87, sensitivity 0.80, and specificity 0.85. For validation cohorts, AUC was 0.87, sensitivity 0.81, and specificity 0.86. Clinical-combined models showed an AUC of 0.93, sensitivity 0.84, and specificity 0.89. Moderate to severe heterogeneity was noted and addressed. Deep-learning models outperformed radiomics models, while clinical-combined models outperformed deep learning-only and radiomics-only models. The average METRICS score was 62.85%. No publication bias was detected. DL and radiomics models showed great potential in predicting HT in AIS patients. However, addressing methodological issues-such as inconsistent reference standards and limited external validation-is essential for the clinical implementation of these models.

预测急性缺血性卒中的出血转化:基于CT/ mri的深度学习和放射组学模型的系统回顾、荟萃分析和方方法论质量评估。
急性缺血性脑卒中(AIS)是死亡率和发病率的主要原因,出血转化(HT)是一种严重的并发症。准确预测高温对优化治疗策略至关重要。本综述评估了深度学习(DL)和放射组学在通过影像学预测AIS患者的临床决策中的准确性和实用性。截至2025年1月23日,对5个数据库(Pubmed、Scopus、Web of Science、Embase、IEEE)进行文献检索。纳入了涉及DL或基于放射组学的ML模型预测AIS患者HT的研究。从训练、验证和临床联合模型中提取数据并分别进行分析。采用随机效应双变量模型计算合并敏感性、特异性和AUC。对于研究的质量评估,使用方法学放射组学评分(METRICS)和QUADAS-2工具。meta分析包括16项研究,共3083名个体参与者。训练队列的合并AUC为0.87,敏感性0.80,特异性0.85。对于验证队列,AUC为0.87,敏感性0.81,特异性0.86。临床联合模型的AUC为0.93,敏感性为0.84,特异性为0.89。注意并解决了中度至重度异质性。深度学习模型优于放射组学模型,而临床结合模型优于仅深度学习和仅放射组学模型。平均METRICS得分为62.85%。未发现发表偏倚。DL和放射组学模型在预测AIS患者HT方面显示出很大的潜力。然而,解决方法学问题——例如不一致的参考标准和有限的外部验证——对于这些模型的临床实施至关重要。
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来源期刊
Emergency Radiology
Emergency Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
4.50%
发文量
98
期刊介绍: To advance and improve the radiologic aspects of emergency careTo establish Emergency Radiology as an area of special interest in the field of diagnostic imagingTo improve methods of education in Emergency RadiologyTo provide, through formal meetings, a mechanism for presentation of scientific papers on various aspects of Emergency Radiology and continuing educationTo promote research in Emergency Radiology by clinical and basic science investigators, including residents and other traineesTo act as the resource body on Emergency Radiology for those interested in emergency patient care Members of the American Society of Emergency Radiology (ASER) receive the Emergency Radiology journal as a benefit of membership!
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