A clinical-radiomics nomogram to predict early neurological deterioration in patients with stroke undergoing intravenous thrombolysis.

IF 2.4
Xiao-Guang Zhang, Shan-Shan Jiang, Dong Zhang, Shu-Hua Chen, Yu-Ming Kong, Yue-Ying Bai, Zhi-Chun Gu, Yun-Hua Yue
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Abstract

Background: Anticipating early neurological deterioration in patients with ischemic stroke undergoing intravenous thrombolysis poses a considerable challenge in clinical practice. This study aimed to develop and validate a diffusion-weighted imaging (DWI)-based clinical-radiomics nomogram for predicting early neurological deterioration in patients with ischemic stroke without large vessel occlusion or hemorrhagic transformation undergoing intravenous thrombolysis.

Methods: A total of 273 patients with stroke were randomly divided into training (n = 192) and validation (n = 81) cohorts at a ratio of 7:3. DWI images taken within 24 hours post-intravenous thrombolysis were used to extract radiological features. The t test, least absolute shrinkage, and selection operator algorithm were used for feature selection. These features were used to create a radiomics score (radscore) for each patient. Combined with the clinical features, a logistic regression model was used to select independent risk factors that were used to construct a clinical-radiomics nomogram. The performance of the nomogram was evaluated using the area under the curve (AUC), calibration, discrimination, and decision curve analysis.

Results: A total of 1307 radiomics features were extracted from each patient's data. A total of 310 radiomics features were found to be stable after being screened by intraclass correlation coefficients. Seven features were included in the construction of the radscore. The AUC of the clinical-radiomics nomogram was 0.89 (95% CI, 0.83-0.95) in the training cohort and 0.95 (95% CI, 0.90-0.99) in the validation cohort. The calibration curve and decision curve analysis indicated favorable calibration and net clinical benefits of the nomogram.

Conclusion: A DWI-based clinical-radiomics nomogram can effectively predict early neurological deterioration in patients with ischemic stroke in the early phase after intravenous thrombolysis.

一种临床放射组学图预测接受静脉溶栓治疗的脑卒中患者早期神经功能恶化。
背景:在临床实践中,预测接受静脉溶栓治疗的缺血性脑卒中患者的早期神经功能恶化是一个相当大的挑战。本研究旨在开发并验证一种基于弥散加权成像(DWI)的临床放射组学图,用于预测接受静脉溶栓治疗的无大血管闭塞或出血转化的缺血性卒中患者的早期神经功能恶化。方法:273例脑卒中患者按7:3的比例随机分为训练组(n = 192)和验证组(n = 81)。采用静脉溶栓后24小时内的DWI图像提取放射学特征。使用t检验、最小绝对收缩和选择算子算法进行特征选择。这些特征用于为每位患者创建放射组学评分(radscore)。结合临床特征,采用logistic回归模型选择独立危险因素,构建临床放射组学线图。使用曲线下面积(AUC)、校准、判别和决策曲线分析来评估nomogram的性能。结果:从每位患者的数据中共提取了1307个放射组学特征。通过类内相关系数筛选,发现有310个放射组学特征是稳定的。在radscore的构建中包含了七个特征。训练组临床-放射组学nomogram AUC为0.89 (95% CI, 0.83-0.95),验证组为0.95 (95% CI, 0.90-0.99)。校正曲线和决策曲线分析显示了良好的校正效果和临床净收益。结论:基于dwi的临床放射组学图可有效预测缺血性脑卒中患者静脉溶栓后早期神经功能恶化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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