聚焦DSC-PWI动态影像组学特征在缺血性脑卒中诊断及预后预测中的作用
A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke.
作者信息
Guo Yingwei, Yang Yingjian, Cao Fengqiu, Wang Mingming, Luo Yu, Guo Jia, Liu Yang, Zeng Xueqiang, Miu Xiaoqiang, Zaman Asim, Lu Jiaxi, Kang Yan
机构信息
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
出版信息
J Clin Med. 2022 Sep 13;11(18):5364. doi: 10.3390/jcm11185364.
BACKGROUND
The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction.
METHODS
The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation.
RESULTS
In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of F in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.
背景
准确检测缺血性中风并预测其神经功能恢复的能力具有重要的临床价值。本研究旨在评估全脑动态放射组学特征（DRF）在缺血性中风检测、神经功能缺损评估及预后预测方面的性能。
方法
采用监督特征选择（套索法）和无监督特征选择方法（五种特征降维算法）生成四个不同DRF组合的实验组。使用十种机器学习模型通过十折交叉验证评估其性能。
结果
在实验组_A中，通过套索法选择的优秀DRF获得了最佳AUC值（中风检测为0.873，美国国立卫生研究院卒中量表[NIHSS]评估为0.795，预后预测为0.818），且显著DRF的性能优于五种特征降维算法。实验组_C中选择的优秀降维DRF获得的AUC比实验组_A中的降维DRF更好，但不如实验组_A中的优秀DRF。将优秀DRF与各降维DRF相结合（实验组_B），在缺血性中风检测（最佳AUC = 0.899）和NIHSS评估（最佳AUC = 0.835）中性能可得到改善，但在预后预测方面失败（最佳AUC = 0.806）。将优秀DRF与优秀降维DRF相结合（实验组_D）时性能可进一步提高，在所有三个评估项目中均获得最高AUC分数（中风检测为0.925，NIHSS评估为0.853，预后预测为0.828）。通过本研究的方法，比较实验组_A中F的最佳AUC与实验组_D中的最佳_AUC，中风检测的AUC增加了19.4%（从0.731增至0.925），NIHSS评估的AUC增加了20.1%（从0.652增至0.853），预后预测的AUC增加了14.9%（从0.679增至0.828）。本研究为治疗前的详细临床诊断和预后预测提供了一种潜在的临床工具。
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