A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke

Yingwei Guo; Yingjian Yang; Fengqiu Cao; Mingming Wang; Yu Luo; Jia Guo; Yang Liu; Xueqiang Zeng; Xiaoqiang Miu; Asim Zaman; Jiaxi Lu; Yan Kang

doi:10.3390/jcm11185364

A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke

J Clin Med. 2022 Sep 13;11(18):5364. doi: 10.3390/jcm11185364.

Authors

Yingwei Guo^{1

2}, Yingjian Yang^{1

2}, Fengqiu Cao^{1

2}, Mingming Wang³, Yu Luo³, Jia Guo⁴, Yang Liu², Xueqiang Zeng^{2

5}, Xiaoqiang Miu^{1

2}, Asim Zaman^{2

5}, Jiaxi Lu^{2

5}, Yan Kang^{1

2

6}

Affiliations

¹ College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
² College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
³ Department of Radiology, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China.
⁴ Department of Psychiatry, Columbia University, New York, NY 10027, USA.
⁵ School of Applied Technology, Shenzhen University, Shenzhen 518060, China.
⁶ Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China.

Abstract

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_t_-test 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.

Keywords: DSC-PWI; Lasso; NIHSS assessment; dimension reduction; dynamic radiomics features; outcome prediction; stroke detection.

Abstract

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